Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations8349
Missing cells10808
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.9 MiB
Average record size in memory2.2 KiB

Variable types

DateTime1
Numeric4
Text22
Categorical11

Alerts

Country of Origin is highly overall correlated with Standard Unit and 2 other fieldsHigh correlation
HS Code is highly overall correlated with Importer City and 1 other fieldsHigh correlation
Importer City is highly overall correlated with HS Code and 6 other fieldsHigh correlation
Importer City- Unified is highly overall correlated with HS Code and 6 other fieldsHigh correlation
Importer Pincode is highly overall correlated with Importer City and 4 other fieldsHigh correlation
Importer Pincode- Unified is highly overall correlated with Importer City and 4 other fieldsHigh correlation
Importer State is highly overall correlated with Importer City and 5 other fieldsHigh correlation
Importer State- Unified is highly overall correlated with Importer City and 5 other fieldsHigh correlation
Month is highly overall correlated with Record IdHigh correlation
Port of Destination is highly overall correlated with Shipment Mode and 2 other fieldsHigh correlation
Record Id is highly overall correlated with MonthHigh correlation
Shipment Mode is highly overall correlated with Port of DestinationHigh correlation
Standard Unit is highly overall correlated with Country of Origin and 7 other fieldsHigh correlation
Unit is highly overall correlated with Country of Origin and 3 other fieldsHigh correlation
Unit Rate Currency is highly overall correlated with Country of Origin and 2 other fieldsHigh correlation
Importer City is highly imbalanced (56.3%)Imbalance
Importer State is highly imbalanced (65.4%)Imbalance
Unit is highly imbalanced (87.8%)Imbalance
Standard Unit is highly imbalanced (95.6%)Imbalance
Unit Rate Currency is highly imbalanced (88.7%)Imbalance
Port of Destination is highly imbalanced (54.5%)Imbalance
Shipment Mode is highly imbalanced (88.8%)Imbalance
Importer City- Unified is highly imbalanced (56.2%)Imbalance
Importer State- Unified is highly imbalanced (65.2%)Imbalance
Importer Add1 has 574 (6.9%) missing valuesMissing
Importer City has 651 (7.8%) missing valuesMissing
Importer State has 594 (7.1%) missing valuesMissing
Importer Phone has 313 (3.7%) missing valuesMissing
Importer E-mail has 1235 (14.8%) missing valuesMissing
Contact Person has 594 (7.1%) missing valuesMissing
Port Of Origin has 2222 (26.6%) missing valuesMissing
Importer Add1- Unified has 575 (6.9%) missing valuesMissing
Importer City- Unified has 742 (8.9%) missing valuesMissing
Importer Pincode- Unified has 137 (1.6%) missing valuesMissing
Importer State- Unified has 692 (8.3%) missing valuesMissing
Importer Phone- Unified has 411 (4.9%) missing valuesMissing
Importer E-Mail- Unified has 1333 (16.0%) missing valuesMissing
Contact Person-Unified has 692 (8.3%) missing valuesMissing
Record Id has unique valuesUnique
Importer Pincode has 557 (6.7%) zerosZeros
Importer Pincode- Unified has 557 (6.7%) zerosZeros

Reproduction

Analysis started2024-10-13 13:56:27.141529
Analysis finished2024-10-13 13:56:32.425088
Duration5.28 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct351
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size388.5 KiB
Minimum2022-01-05 00:00:00
Maximum2023-12-03 00:00:00
2024-10-13T19:26:32.521469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:32.665872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

HS Code
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29078808
Minimum29024100
Maximum29420090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.5 KiB
2024-10-13T19:26:32.803958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum29024100
5-th percentile29025000
Q129025000
median29025000
Q329153200
95-th percentile29153300
Maximum29420090
Range395990
Interquartile range (IQR)128200

Descriptive statistics

Standard deviation63968.541
Coefficient of variation (CV)0.0021998337
Kurtosis-1.5348225
Mean29078808
Median Absolute Deviation (MAD)0
Skewness0.40594338
Sum2.4277897 × 1011
Variance4.0919742 × 109
MonotonicityNot monotonic
2024-10-13T19:26:32.936943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
29025000 4520
54.1%
29153200 2778
33.3%
29153300 500
 
6.0%
29024100 250
 
3.0%
29153999 114
 
1.4%
29024300 73
 
0.9%
29159099 55
 
0.7%
29024400 10
 
0.1%
29094990 5
 
0.1%
29141300 5
 
0.1%
Other values (27) 39
 
0.5%
ValueCountFrequency (%)
29024100 250
 
3.0%
29024300 73
 
0.9%
29024400 10
 
0.1%
29025000 4520
54.1%
29029090 3
 
< 0.1%
29051490 3
 
< 0.1%
29052210 1
 
< 0.1%
29054290 1
 
< 0.1%
29061990 1
 
< 0.1%
29072990 1
 
< 0.1%
ValueCountFrequency (%)
29420090 1
 
< 0.1%
29389090 1
 
< 0.1%
29339990 4
< 0.1%
29332920 1
 
< 0.1%
29319090 1
 
< 0.1%
29309099 2
< 0.1%
29261000 1
 
< 0.1%
29232090 1
 
< 0.1%
29222913 1
 
< 0.1%
29182190 1
 
< 0.1%
Distinct662
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size992.1 KiB
2024-10-13T19:26:33.150772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length148
Median length136
Mean length33.029824
Min length11

Characters and Unicode

Total characters275766
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)2.7%

Sample

1st rowSTYRENE MONOMER IN BULK
2nd rowSTYRENE MONOMER SM
3rd rowVINYL ACETATE MONOMER
4th rowSTYRENE MONOMERSM
5th rowSTYRENE MONOMER
ValueCountFrequency (%)
monomer 6615
16.9%
styrene 4520
11.5%
bulk 3825
 
9.8%
acetate 3468
 
8.9%
in 3372
 
8.6%
vinyl 2785
 
7.1%
ref 1414
 
3.6%
no 1081
 
2.8%
aifta 824
 
2.1%
liquid 509
 
1.3%
Other values (943) 10748
27.4%
2024-10-13T19:26:33.520847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32489
 
11.8%
E 26972
 
9.8%
N 20889
 
7.6%
O 17986
 
6.5%
M 16272
 
5.9%
T 16136
 
5.9%
R 14289
 
5.2%
2 12897
 
4.7%
A 11157
 
4.0%
0 10300
 
3.7%
Other values (59) 96379
34.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 190125
68.9%
Decimal Number 41388
 
15.0%
Space Separator 32489
 
11.8%
Other Punctuation 7616
 
2.8%
Dash Punctuation 3275
 
1.2%
Lowercase Letter 816
 
0.3%
Math Symbol 37
 
< 0.1%
Open Punctuation 10
 
< 0.1%
Close Punctuation 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 26972
14.2%
N 20889
11.0%
O 17986
9.5%
M 16272
8.6%
T 16136
8.5%
R 14289
 
7.5%
A 11157
 
5.9%
I 9748
 
5.1%
L 8719
 
4.6%
Y 8543
 
4.5%
Other values (15) 39414
20.7%
Lowercase Letter
ValueCountFrequency (%)
a 137
16.8%
t 128
15.7%
e 87
10.7%
l 71
8.7%
y 68
8.3%
n 66
8.1%
s 54
 
6.6%
u 42
 
5.1%
c 42
 
5.1%
r 31
 
3.8%
Other values (8) 90
11.0%
Decimal Number
ValueCountFrequency (%)
2 12897
31.2%
0 10300
24.9%
1 5346
12.9%
5 2393
 
5.8%
3 2134
 
5.2%
4 2079
 
5.0%
6 1680
 
4.1%
7 1674
 
4.0%
9 1505
 
3.6%
8 1380
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 6883
90.4%
/ 417
 
5.5%
: 146
 
1.9%
% 74
 
1.0%
, 48
 
0.6%
& 40
 
0.5%
' 6
 
0.1%
? 1
 
< 0.1%
* 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 20
54.1%
| 15
40.5%
= 2
 
5.4%
Space Separator
ValueCountFrequency (%)
32489
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3275
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 10
100.0%
Close Punctuation
ValueCountFrequency (%)
] 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 190941
69.2%
Common 84825
30.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 26972
14.1%
N 20889
10.9%
O 17986
9.4%
M 16272
8.5%
T 16136
8.5%
R 14289
 
7.5%
A 11157
 
5.8%
I 9748
 
5.1%
L 8719
 
4.6%
Y 8543
 
4.5%
Other values (33) 40230
21.1%
Common
ValueCountFrequency (%)
32489
38.3%
2 12897
 
15.2%
0 10300
 
12.1%
. 6883
 
8.1%
1 5346
 
6.3%
- 3275
 
3.9%
5 2393
 
2.8%
3 2134
 
2.5%
4 2079
 
2.5%
6 1680
 
2.0%
Other values (16) 5349
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 275766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32489
 
11.8%
E 26972
 
9.8%
N 20889
 
7.6%
O 17986
 
6.5%
M 16272
 
5.9%
T 16136
 
5.9%
R 14289
 
5.2%
2 12897
 
4.7%
A 11157
 
4.0%
0 10300
 
3.7%
Other values (59) 96379
34.9%
Distinct186
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size924.1 KiB
2024-10-13T19:26:33.739181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length48
Median length43
Mean length24.695413
Min length8

Characters and Unicode

Total characters206182
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)0.6%

Sample

1st rowSUPREME PETROCHEM LTD
2nd rowSHIVA PERFORMANCE MATERIALS PRIVATE LIMITED
3rd rowJESONS INDUSTRIES LIMITED
4th rowINEOS STYROLUTION INDIA LIMITED
5th rowJESONS INDUSTRIES LIMITED
ValueCountFrequency (%)
ltd 3752
 
12.2%
limited 3062
 
10.0%
pvt 1804
 
5.9%
industries 1286
 
4.2%
chemicals 1157
 
3.8%
india 998
 
3.2%
visen 976
 
3.2%
private 886
 
2.9%
polymers 674
 
2.2%
petrochem 590
 
1.9%
Other values (295) 15586
50.7%
2024-10-13T19:26:34.114002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22422
 
10.9%
I 22393
 
10.9%
E 19096
 
9.3%
T 14794
 
7.2%
L 13549
 
6.6%
S 12853
 
6.2%
A 11989
 
5.8%
D 11411
 
5.5%
R 10727
 
5.2%
N 10183
 
4.9%
Other values (32) 56765
27.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 183696
89.1%
Space Separator 22422
 
10.9%
Lowercase Letter 54
 
< 0.1%
Dash Punctuation 6
 
< 0.1%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 22393
12.2%
E 19096
10.4%
T 14794
 
8.1%
L 13549
 
7.4%
S 12853
 
7.0%
A 11989
 
6.5%
D 11411
 
6.2%
R 10727
 
5.8%
N 10183
 
5.5%
C 9734
 
5.3%
Other values (16) 46967
25.6%
Lowercase Letter
ValueCountFrequency (%)
n 12
22.2%
i 8
14.8%
t 8
14.8%
e 8
14.8%
a 4
 
7.4%
g 2
 
3.7%
y 2
 
3.7%
r 2
 
3.7%
o 2
 
3.7%
l 2
 
3.7%
Other values (2) 4
 
7.4%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%
Space Separator
ValueCountFrequency (%)
22422
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 183750
89.1%
Common 22432
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 22393
12.2%
E 19096
10.4%
T 14794
 
8.1%
L 13549
 
7.4%
S 12853
 
7.0%
A 11989
 
6.5%
D 11411
 
6.2%
R 10727
 
5.8%
N 10183
 
5.5%
C 9734
 
5.3%
Other values (28) 47021
25.6%
Common
ValueCountFrequency (%)
22422
> 99.9%
- 6
 
< 0.1%
1 2
 
< 0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22422
 
10.9%
I 22393
 
10.9%
E 19096
 
9.3%
T 14794
 
7.2%
L 13549
 
6.6%
S 12853
 
6.2%
A 11989
 
5.8%
D 11411
 
5.5%
R 10727
 
5.2%
N 10183
 
4.9%
Other values (32) 56765
27.5%

Importer Add1
Text

MISSING 

Distinct163
Distinct (%)2.1%
Missing574
Missing (%)6.9%
Memory size1.1 MiB
2024-10-13T19:26:34.341168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length115
Median length83
Mean length60.239486
Min length18

Characters and Unicode

Total characters468362
Distinct characters53
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.5%

Sample

1st rowSOLITAIRE CORPORATE PARK,BLDG 11 5 TH FLR GURU HARGOVINJI MARG CHAKALAANDHERI WEST
2nd row904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WEST
3rd row5TH FLOOR OHM HOUSE-II,,OHM BUSINE SS PARK, SUBHANPURA,
4th row904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WEST
5th row5TH FLOOR OHM HOUSE-II,,OHM BUSINE SS PARK, SUBHANPURA,
ValueCountFrequency (%)
3716
 
5.3%
andheri 2191
 
3.1%
floor 1602
 
2.3%
no 1547
 
2.2%
point 1349
 
1.9%
plot 1279
 
1.8%
road 1102
 
1.6%
w 1054
 
1.5%
west 1050
 
1.5%
western 1032
 
1.5%
Other values (806) 54570
77.4%
2024-10-13T19:26:34.714890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
62723
 
13.4%
A 41509
 
8.9%
, 29097
 
6.2%
R 28376
 
6.1%
N 26884
 
5.7%
O 25153
 
5.4%
E 24206
 
5.2%
I 20563
 
4.4%
T 20509
 
4.4%
H 16244
 
3.5%
Other values (43) 173098
37.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 328392
70.1%
Space Separator 62723
 
13.4%
Other Punctuation 41022
 
8.8%
Decimal Number 30714
 
6.6%
Dash Punctuation 2514
 
0.5%
Close Punctuation 1313
 
0.3%
Open Punctuation 1313
 
0.3%
Lowercase Letter 342
 
0.1%
Modifier Symbol 29
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 41509
12.6%
R 28376
 
8.6%
N 26884
 
8.2%
O 25153
 
7.7%
E 24206
 
7.4%
I 20563
 
6.3%
T 20509
 
6.2%
H 16244
 
4.9%
S 15857
 
4.8%
L 15335
 
4.7%
Other values (16) 93756
28.6%
Decimal Number
ValueCountFrequency (%)
1 6293
20.5%
5 5720
18.6%
0 4273
13.9%
4 3335
10.9%
2 2885
9.4%
7 2146
 
7.0%
3 1896
 
6.2%
8 1695
 
5.5%
9 1371
 
4.5%
6 1100
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 29097
70.9%
. 8559
 
20.9%
/ 1636
 
4.0%
& 1136
 
2.8%
* 510
 
1.2%
; 84
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
n 86
25.1%
b 84
24.6%
s 84
24.6%
p 84
24.6%
a 2
 
0.6%
d 2
 
0.6%
Space Separator
ValueCountFrequency (%)
62723
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2514
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1313
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1313
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 328734
70.2%
Common 139628
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 41509
12.6%
R 28376
 
8.6%
N 26884
 
8.2%
O 25153
 
7.7%
E 24206
 
7.4%
I 20563
 
6.3%
T 20509
 
6.2%
H 16244
 
4.9%
S 15857
 
4.8%
L 15335
 
4.7%
Other values (22) 94098
28.6%
Common
ValueCountFrequency (%)
62723
44.9%
, 29097
20.8%
. 8559
 
6.1%
1 6293
 
4.5%
5 5720
 
4.1%
0 4273
 
3.1%
4 3335
 
2.4%
2 2885
 
2.1%
- 2514
 
1.8%
7 2146
 
1.5%
Other values (11) 12083
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62723
 
13.4%
A 41509
 
8.9%
, 29097
 
6.2%
R 28376
 
6.1%
N 26884
 
5.7%
O 25153
 
5.4%
E 24206
 
5.2%
I 20563
 
4.4%
T 20509
 
4.4%
H 16244
 
3.5%
Other values (43) 173098
37.0%

Importer City
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct43
Distinct (%)0.6%
Missing651
Missing (%)7.8%
Memory size774.5 KiB
MUMBAI
3851 
Mumbai
2065 
KOLKATA
 
365
VADODARA
 
275
New Delhi
 
230
Other values (38)
912 

Length

Max length13
Median length6
Mean length6.2922837
Min length4

Characters and Unicode

Total characters48438
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowMUMBAI
2nd rowMUMBAI
3rd rowVADODARA
4th rowMUMBAI
5th rowVADODARA

Common Values

ValueCountFrequency (%)
MUMBAI 3851
46.1%
Mumbai 2065
24.7%
KOLKATA 365
 
4.4%
VADODARA 275
 
3.3%
New Delhi 230
 
2.8%
Delhi 200
 
2.4%
FARIDABAD 121
 
1.4%
NOIDA 70
 
0.8%
BANGALORE 64
 
0.8%
DELHI 64
 
0.8%
Other values (33) 393
 
4.7%
(Missing) 651
 
7.8%

Length

2024-10-13T19:26:34.848741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai 5916
74.6%
delhi 494
 
6.2%
kolkata 377
 
4.8%
vadodara 275
 
3.5%
new 230
 
2.9%
faridabad 121
 
1.5%
noida 70
 
0.9%
bangalore 66
 
0.8%
kanpur 62
 
0.8%
ahmedabad 41
 
0.5%
Other values (28) 278
 
3.5%

Most occurring characters

ValueCountFrequency (%)
M 9855
20.3%
A 6247
12.9%
I 4217
8.7%
B 4128
8.5%
U 3889
 
8.0%
i 2550
 
5.3%
a 2321
 
4.8%
u 2206
 
4.6%
b 2085
 
4.3%
m 2067
 
4.3%
Other values (35) 8873
18.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 34545
71.3%
Lowercase Letter 13653
 
28.2%
Space Separator 234
 
0.5%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 9855
28.5%
A 6247
18.1%
I 4217
12.2%
B 4128
11.9%
U 3889
 
11.3%
D 1478
 
4.3%
O 843
 
2.4%
K 840
 
2.4%
R 551
 
1.6%
L 542
 
1.6%
Other values (12) 1955
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
i 2550
18.7%
a 2321
17.0%
u 2206
16.2%
b 2085
15.3%
m 2067
15.1%
e 699
 
5.1%
h 487
 
3.6%
l 446
 
3.3%
w 230
 
1.7%
r 145
 
1.1%
Other values (10) 417
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 4
66.7%
, 2
33.3%
Space Separator
ValueCountFrequency (%)
234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48198
99.5%
Common 240
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 9855
20.4%
A 6247
13.0%
I 4217
8.7%
B 4128
8.6%
U 3889
 
8.1%
i 2550
 
5.3%
a 2321
 
4.8%
u 2206
 
4.6%
b 2085
 
4.3%
m 2067
 
4.3%
Other values (32) 8633
17.9%
Common
ValueCountFrequency (%)
234
97.5%
. 4
 
1.7%
, 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 9855
20.3%
A 6247
12.9%
I 4217
8.7%
B 4128
8.5%
U 3889
 
8.0%
i 2550
 
5.3%
a 2321
 
4.8%
u 2206
 
4.6%
b 2085
 
4.3%
m 2067
 
4.3%
Other values (35) 8873
18.3%

Importer Pincode
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)1.2%
Missing39
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean362332.58
Minimum0
Maximum700091
Zeros557
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size388.5 KiB
2024-10-13T19:26:34.966996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1400009
median400053
Q3400066
95-th percentile600040
Maximum700091
Range700091
Interquartile range (IQR)57

Descriptive statistics

Standard deviation145749.76
Coefficient of variation (CV)0.40225409
Kurtosis1.6616603
Mean362332.58
Median Absolute Deviation (MAD)40
Skewness-0.82570795
Sum3.0109837 × 109
Variance2.1242992 × 1010
MonotonicityNot monotonic
2024-10-13T19:26:35.105453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400021 1348
16.1%
400058 1327
15.9%
400093 1089
13.0%
400066 579
 
6.9%
0 557
 
6.7%
400009 325
 
3.9%
700017 282
 
3.4%
400059 245
 
2.9%
390023 240
 
2.9%
400011 219
 
2.6%
Other values (91) 2099
25.1%
ValueCountFrequency (%)
0 557
6.7%
110001 74
 
0.9%
110002 138
 
1.7%
110005 9
 
0.1%
110020 94
 
1.1%
110028 11
 
0.1%
110033 70
 
0.8%
110034 28
 
0.3%
110035 5
 
0.1%
110057 1
 
< 0.1%
ValueCountFrequency (%)
700091 81
 
1.0%
700071 12
 
0.1%
700017 282
3.4%
700001 2
 
< 0.1%
682310 3
 
< 0.1%
603105 7
 
0.1%
600119 17
 
0.2%
600040 14
 
0.2%
600016 9
 
0.1%
562106 16
 
0.2%

Importer State
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct24
Distinct (%)0.3%
Missing594
Missing (%)7.1%
Memory size805.1 KiB
MAHARASHTRA
5900 
WEST BENGAL
 
365
GUJARAT
 
347
DELHI
 
264
Delhi
 
230
Other values (19)
649 

Length

Max length14
Median length11
Mean length10.332044
Min length5

Characters and Unicode

Total characters80125
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMAHARASHTRA
2nd rowMAHARASHTRA
3rd rowGUJARAT
4th rowMAHARASHTRA
5th rowGUJARAT

Common Values

ValueCountFrequency (%)
MAHARASHTRA 5900
70.7%
WEST BENGAL 365
 
4.4%
GUJARAT 347
 
4.2%
DELHI 264
 
3.2%
Delhi 230
 
2.8%
UTTAR PRADESH 145
 
1.7%
Maharashtra 134
 
1.6%
HARYANA 121
 
1.4%
KARNATAKA 68
 
0.8%
TAMIL NADU 47
 
0.6%
Other values (14) 134
 
1.6%
(Missing) 594
 
7.1%

Length

2024-10-13T19:26:35.236142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maharashtra 6034
72.4%
delhi 494
 
5.9%
bengal 377
 
4.5%
west 377
 
4.5%
gujarat 361
 
4.3%
pradesh 151
 
1.8%
uttar 145
 
1.7%
haryana 121
 
1.5%
karnataka 70
 
0.8%
punjab 67
 
0.8%
Other values (8) 133
 
1.6%

Most occurring characters

ValueCountFrequency (%)
A 25815
32.2%
R 12649
15.8%
H 12350
15.4%
T 7043
 
8.8%
S 6424
 
8.0%
M 6083
 
7.6%
E 1160
 
1.4%
G 741
 
0.9%
D 695
 
0.9%
L 693
 
0.9%
Other values (26) 6472
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 76912
96.0%
Lowercase Letter 2638
 
3.3%
Space Separator 575
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 25815
33.6%
R 12649
16.4%
H 12350
16.1%
T 7043
 
9.2%
S 6424
 
8.4%
M 6083
 
7.9%
E 1160
 
1.5%
G 741
 
1.0%
D 695
 
0.9%
L 693
 
0.9%
Other values (10) 3259
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
a 627
23.8%
h 505
19.1%
r 288
10.9%
e 258
9.8%
l 244
 
9.2%
i 230
 
8.7%
t 165
 
6.3%
s 151
 
5.7%
n 47
 
1.8%
j 41
 
1.6%
Other values (5) 82
 
3.1%
Space Separator
ValueCountFrequency (%)
575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79550
99.3%
Common 575
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 25815
32.5%
R 12649
15.9%
H 12350
15.5%
T 7043
 
8.9%
S 6424
 
8.1%
M 6083
 
7.6%
E 1160
 
1.5%
G 741
 
0.9%
D 695
 
0.9%
L 693
 
0.9%
Other values (25) 5897
 
7.4%
Common
ValueCountFrequency (%)
575
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 25815
32.2%
R 12649
15.8%
H 12350
15.4%
T 7043
 
8.8%
S 6424
 
8.0%
M 6083
 
7.6%
E 1160
 
1.4%
G 741
 
0.9%
D 695
 
0.9%
L 693
 
0.9%
Other values (26) 6472
 
8.1%

Importer Phone
Text

MISSING 

Distinct209
Distinct (%)2.6%
Missing313
Missing (%)3.7%
Memory size968.6 KiB
2024-10-13T19:26:35.414529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length70
Median length61
Mean length31.985316
Min length1

Characters and Unicode

Total characters257034
Distinct characters26
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)0.6%

Sample

1st row919820037286
2nd row919819883380
3rd row919076307694
4th row919819883380
5th row919076307694
ValueCountFrequency (%)
022-66443333 976
 
11.7%
28357532 510
 
6.1%
917666986831 497
 
6.0%
65102323 475
 
5.7%
919820037286 406
 
4.9%
919870423455 364
 
4.4%
919773333523 351
 
4.2%
919322884854 315
 
3.8%
0 293
 
3.5%
919820689243 287
 
3.4%
Other values (170) 3878
46.4%
2024-10-13T19:26:35.768748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
164051
63.8%
3 14011
 
5.5%
9 12402
 
4.8%
2 11335
 
4.4%
8 10647
 
4.1%
1 9188
 
3.6%
6 8492
 
3.3%
0 7395
 
2.9%
4 6232
 
2.4%
7 5648
 
2.2%
Other values (16) 7633
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 164051
63.8%
Decimal Number 90535
35.2%
Dash Punctuation 1415
 
0.6%
Lowercase Letter 563
 
0.2%
Other Punctuation 281
 
0.1%
Uppercase Letter 189
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14011
15.5%
9 12402
13.7%
2 11335
12.5%
8 10647
11.8%
1 9188
10.1%
6 8492
9.4%
0 7395
8.2%
4 6232
6.9%
7 5648
6.2%
5 5185
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
x 183
32.5%
f 183
32.5%
a 183
32.5%
n 7
 
1.2%
o 7
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
F 51
27.0%
A 51
27.0%
X 51
27.0%
N 18
 
9.5%
O 18
 
9.5%
Other Punctuation
ValueCountFrequency (%)
/ 212
75.4%
, 43
 
15.3%
. 25
 
8.9%
: 1
 
0.4%
Space Separator
ValueCountFrequency (%)
164051
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1415
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 256282
99.7%
Latin 752
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
164051
64.0%
3 14011
 
5.5%
9 12402
 
4.8%
2 11335
 
4.4%
8 10647
 
4.2%
1 9188
 
3.6%
6 8492
 
3.3%
0 7395
 
2.9%
4 6232
 
2.4%
7 5648
 
2.2%
Other values (6) 6881
 
2.7%
Latin
ValueCountFrequency (%)
x 183
24.3%
f 183
24.3%
a 183
24.3%
F 51
 
6.8%
A 51
 
6.8%
X 51
 
6.8%
N 18
 
2.4%
O 18
 
2.4%
n 7
 
0.9%
o 7
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 257034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164051
63.8%
3 14011
 
5.5%
9 12402
 
4.8%
2 11335
 
4.4%
8 10647
 
4.1%
1 9188
 
3.6%
6 8492
 
3.3%
0 7395
 
2.9%
4 6232
 
2.4%
7 5648
 
2.2%
Other values (16) 7633
 
3.0%

Importer E-mail
Text

MISSING 

Distinct149
Distinct (%)2.1%
Missing1235
Missing (%)14.8%
Memory size857.4 KiB
2024-10-13T19:26:35.954019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length55
Median length35
Mean length22.323166
Min length12

Characters and Unicode

Total characters158807
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.5%

Sample

1st rowdevdas_chandran@spl.co.in
2nd rowmanish.rathod@jesons.net
3rd rowsunilrao@supertechimpex.com
4th rowmanish.rathod@jesons.net
5th rowsunilrao@supertechimpex.com
ValueCountFrequency (%)
info@visen.net 976
 
13.5%
vijay.chalke@pidilite.co.in 510
 
7.1%
nirmal@nikhiladhesives.com 497
 
6.9%
devdas_chandran@spl.co.in 406
 
5.6%
kiran@cjshahgroup.com 364
 
5.1%
shnair@bhansaliabs.com 351
 
4.9%
asha@chokhanigroup.com 315
 
4.4%
sasee@sanjaychemindia.com 287
 
4.0%
santosh@kljindia.com 282
 
3.9%
sunilrao@supertechimpex.com 240
 
3.3%
Other values (142) 2977
41.3%
2024-10-13T19:26:36.269324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14773
 
9.3%
i 13983
 
8.8%
n 11841
 
7.5%
s 10613
 
6.7%
c 10249
 
6.5%
o 10066
 
6.3%
. 9761
 
6.1%
e 9581
 
6.0%
h 8622
 
5.4%
m 8317
 
5.2%
Other values (50) 51001
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 140334
88.4%
Other Punctuation 16999
 
10.7%
Decimal Number 637
 
0.4%
Connector Punctuation 449
 
0.3%
Uppercase Letter 153
 
0.1%
Space Separator 139
 
0.1%
Dash Punctuation 96
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14773
10.5%
i 13983
 
10.0%
n 11841
 
8.4%
s 10613
 
7.6%
c 10249
 
7.3%
o 10066
 
7.2%
e 9581
 
6.8%
h 8622
 
6.1%
m 8317
 
5.9%
r 6130
 
4.4%
Other values (15) 36159
25.8%
Uppercase Letter
ValueCountFrequency (%)
N 29
19.0%
E 21
13.7%
O 18
11.8%
I 16
10.5%
T 9
 
5.9%
S 8
 
5.2%
L 8
 
5.2%
R 8
 
5.2%
M 8
 
5.2%
P 8
 
5.2%
Other values (8) 20
13.1%
Decimal Number
ValueCountFrequency (%)
2 190
29.8%
1 140
22.0%
5 131
20.6%
0 83
13.0%
9 32
 
5.0%
7 32
 
5.0%
4 12
 
1.9%
6 9
 
1.4%
3 8
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 9761
57.4%
@ 7198
42.3%
/ 29
 
0.2%
: 9
 
0.1%
, 2
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 449
100.0%
Space Separator
ValueCountFrequency (%)
139
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 140487
88.5%
Common 18320
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14773
10.5%
i 13983
 
10.0%
n 11841
 
8.4%
s 10613
 
7.6%
c 10249
 
7.3%
o 10066
 
7.2%
e 9581
 
6.8%
h 8622
 
6.1%
m 8317
 
5.9%
r 6130
 
4.4%
Other values (33) 36312
25.8%
Common
ValueCountFrequency (%)
. 9761
53.3%
@ 7198
39.3%
_ 449
 
2.5%
2 190
 
1.0%
1 140
 
0.8%
139
 
0.8%
5 131
 
0.7%
- 96
 
0.5%
0 83
 
0.5%
9 32
 
0.2%
Other values (7) 101
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14773
 
9.3%
i 13983
 
8.8%
n 11841
 
7.5%
s 10613
 
6.7%
c 10249
 
6.5%
o 10066
 
6.3%
. 9761
 
6.1%
e 9581
 
6.0%
h 8622
 
5.4%
m 8317
 
5.2%
Other values (50) 51001
32.1%

Contact Person
Text

MISSING 

Distinct157
Distinct (%)2.0%
Missing594
Missing (%)7.1%
Memory size915.0 KiB
2024-10-13T19:26:36.535717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length35
Mean length26.681238
Min length6

Characters and Unicode

Total characters206913
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.5%

Sample

1st rowM S RAMACHANDRAN
2nd rowMR DHIRESH GOSALIA
3rd rowSANJIV VASUDEVA
4th rowMR DHIRESH GOSALIA
5th rowSANJIV VASUDEVA
ValueCountFrequency (%)
t.p.ramachandran 976
 
5.7%
m 757
 
4.4%
gupta 680
 
4.0%
sanghavi 547
 
3.2%
raman 510
 
3.0%
tandon 510
 
3.0%
jayantilal 497
 
2.9%
umesh 497
 
2.9%
jinesh 475
 
2.8%
m.shah 475
 
2.8%
Other values (306) 11118
65.2%
2024-10-13T19:26:36.934841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
94847
45.8%
A 24494
 
11.8%
N 10492
 
5.1%
R 8278
 
4.0%
H 7760
 
3.8%
M 7063
 
3.4%
S 6540
 
3.2%
I 6183
 
3.0%
T 4909
 
2.4%
L 4407
 
2.1%
Other values (17) 31940
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 108576
52.5%
Space Separator 94847
45.8%
Other Punctuation 3488
 
1.7%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 24494
22.6%
N 10492
9.7%
R 8278
 
7.6%
H 7760
 
7.1%
M 7063
 
6.5%
S 6540
 
6.0%
I 6183
 
5.7%
T 4909
 
4.5%
L 4407
 
4.1%
D 3931
 
3.6%
Other values (13) 24519
22.6%
Other Punctuation
ValueCountFrequency (%)
. 3486
99.9%
, 2
 
0.1%
Space Separator
ValueCountFrequency (%)
94847
100.0%
Decimal Number
ValueCountFrequency (%)
0 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108576
52.5%
Common 98337
47.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 24494
22.6%
N 10492
9.7%
R 8278
 
7.6%
H 7760
 
7.1%
M 7063
 
6.5%
S 6540
 
6.0%
I 6183
 
5.7%
T 4909
 
4.5%
L 4407
 
4.1%
D 3931
 
3.6%
Other values (13) 24519
22.6%
Common
ValueCountFrequency (%)
94847
96.5%
. 3486
 
3.5%
0 2
 
< 0.1%
, 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
94847
45.8%
A 24494
 
11.8%
N 10492
 
5.1%
R 8278
 
4.0%
H 7760
 
3.8%
M 7063
 
3.4%
S 6540
 
3.2%
I 6183
 
3.0%
T 4909
 
2.4%
L 4407
 
2.1%
Other values (17) 31940
 
15.4%
Distinct203
Distinct (%)2.4%
Missing2
Missing (%)< 0.1%
Memory size935.4 KiB
2024-10-13T19:26:37.192632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length50
Median length43
Mean length26.087936
Min length8

Characters and Unicode

Total characters217756
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.7%

Sample

1st rowSABIC ASIA PACIFIC PTE LTD
2nd rowM S SHELL INTERNATIONAL EASTERN TRA
3rd rowSIPCHEM MARKETING COMPANY
4th rowM S SABIC ASIA PACIFIC PTE LTD
5th rowSHELL INTERNATIONAL EASTERN TRADING
ValueCountFrequency (%)
ltd 3581
 
10.1%
international 2441
 
6.9%
pte 2367
 
6.7%
s 2350
 
6.6%
m 2149
 
6.0%
company 1808
 
5.1%
shell 1477
 
4.1%
eastern 1461
 
4.1%
trading 1239
 
3.5%
marketing 998
 
2.8%
Other values (263) 15720
44.2%
2024-10-13T19:26:37.589522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27314
12.5%
E 20350
 
9.3%
A 18767
 
8.6%
T 18317
 
8.4%
N 18139
 
8.3%
I 14958
 
6.9%
L 13714
 
6.3%
R 12401
 
5.7%
C 11499
 
5.3%
S 10616
 
4.9%
Other values (36) 51681
23.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 188239
86.4%
Space Separator 27314
 
12.5%
Lowercase Letter 2156
 
1.0%
Dash Punctuation 47
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 20350
10.8%
A 18767
10.0%
T 18317
9.7%
N 18139
9.6%
I 14958
 
7.9%
L 13714
 
7.3%
R 12401
 
6.6%
C 11499
 
6.1%
S 10616
 
5.6%
O 8804
 
4.7%
Other values (15) 40674
21.6%
Lowercase Letter
ValueCountFrequency (%)
i 312
14.5%
a 251
11.6%
e 247
11.5%
c 215
10.0%
t 169
 
7.8%
n 139
 
6.4%
m 119
 
5.5%
p 114
 
5.3%
r 87
 
4.0%
k 79
 
3.7%
Other values (9) 424
19.7%
Space Separator
ValueCountFrequency (%)
27314
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 190395
87.4%
Common 27361
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 20350
10.7%
A 18767
9.9%
T 18317
9.6%
N 18139
9.5%
I 14958
 
7.9%
L 13714
 
7.2%
R 12401
 
6.5%
C 11499
 
6.0%
S 10616
 
5.6%
O 8804
 
4.6%
Other values (34) 42830
22.5%
Common
ValueCountFrequency (%)
27314
99.8%
- 47
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 217756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27314
12.5%
E 20350
 
9.3%
A 18767
 
8.6%
T 18317
 
8.4%
N 18139
 
8.3%
I 14958
 
6.9%
L 13714
 
6.3%
R 12401
 
5.7%
C 11499
 
5.3%
S 10616
 
4.9%
Other values (36) 51681
23.7%

QTY
Text

Distinct2433
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size769.4 KiB
2024-10-13T19:26:37.873099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.7209247
Min length4

Characters and Unicode

Total characters47764
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2041 ?
Unique (%)24.4%

Sample

1st row4,000.40
2nd row100.00
3rd row100.00
4th row2,500.25
5th row485.14
ValueCountFrequency (%)
100.00 990
 
11.9%
50.00 598
 
7.2%
200.00 545
 
6.5%
25.00 338
 
4.0%
500.00 320
 
3.8%
60.00 225
 
2.7%
150.00 200
 
2.4%
300.00 173
 
2.1%
42.00 166
 
2.0%
22.00 120
 
1.4%
Other values (2423) 4674
56.0%
2024-10-13T19:26:38.268854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20021
41.9%
. 8349
17.5%
1 3663
 
7.7%
5 3327
 
7.0%
2 3179
 
6.7%
4 1792
 
3.8%
9 1521
 
3.2%
3 1443
 
3.0%
6 1253
 
2.6%
8 1232
 
2.6%
Other values (2) 1984
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38615
80.8%
Other Punctuation 9149
 
19.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20021
51.8%
1 3663
 
9.5%
5 3327
 
8.6%
2 3179
 
8.2%
4 1792
 
4.6%
9 1521
 
3.9%
3 1443
 
3.7%
6 1253
 
3.2%
8 1232
 
3.2%
7 1184
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 8349
91.3%
, 800
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 47764
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20021
41.9%
. 8349
17.5%
1 3663
 
7.7%
5 3327
 
7.0%
2 3179
 
6.7%
4 1792
 
3.8%
9 1521
 
3.2%
3 1443
 
3.0%
6 1253
 
2.6%
8 1232
 
2.6%
Other values (2) 1984
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20021
41.9%
. 8349
17.5%
1 3663
 
7.7%
5 3327
 
7.0%
2 3179
 
6.7%
4 1792
 
3.8%
9 1521
 
3.2%
3 1443
 
3.0%
6 1253
 
2.6%
8 1232
 
2.6%
Other values (2) 1984
 
4.2%

Unit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size747.2 KiB
MTS
7955 
KGS
 
327
NOS
 
46
LTR
 
16
GMS
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25047
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMTS
2nd rowMTS
3rd rowMTS
4th rowMTS
5th rowMTS

Common Values

ValueCountFrequency (%)
MTS 7955
95.3%
KGS 327
 
3.9%
NOS 46
 
0.6%
LTR 16
 
0.2%
GMS 4
 
< 0.1%
PCS 1
 
< 0.1%

Length

2024-10-13T19:26:38.402638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T19:26:38.505476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
mts 7955
95.3%
kgs 327
 
3.9%
nos 46
 
0.6%
ltr 16
 
0.2%
gms 4
 
< 0.1%
pcs 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 8333
33.3%
T 7971
31.8%
M 7959
31.8%
G 331
 
1.3%
K 327
 
1.3%
N 46
 
0.2%
O 46
 
0.2%
L 16
 
0.1%
R 16
 
0.1%
P 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25047
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 8333
33.3%
T 7971
31.8%
M 7959
31.8%
G 331
 
1.3%
K 327
 
1.3%
N 46
 
0.2%
O 46
 
0.2%
L 16
 
0.1%
R 16
 
0.1%
P 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 25047
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 8333
33.3%
T 7971
31.8%
M 7959
31.8%
G 331
 
1.3%
K 327
 
1.3%
N 46
 
0.2%
O 46
 
0.2%
L 16
 
0.1%
R 16
 
0.1%
P 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 8333
33.3%
T 7971
31.8%
M 7959
31.8%
G 331
 
1.3%
K 327
 
1.3%
N 46
 
0.2%
O 46
 
0.2%
L 16
 
0.1%
R 16
 
0.1%
P 1
 
< 0.1%
Distinct2454
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size804.2 KiB
2024-10-13T19:26:38.720699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length12
Mean length9.9859863
Min length4

Characters and Unicode

Total characters83373
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2042 ?
Unique (%)24.5%

Sample

1st row40,00,400.00
2nd row1,00,000.00
3rd row1,00,000.00
4th row25,00,250.00
5th row4,85,140.00
ValueCountFrequency (%)
1,00,000.00 987
 
11.8%
50,000.00 591
 
7.1%
2,00,000.00 544
 
6.5%
25,000.00 332
 
4.0%
5,00,000.00 319
 
3.8%
60,000.00 222
 
2.7%
1,50,000.00 200
 
2.4%
3,00,000.00 173
 
2.1%
42,000.00 166
 
2.0%
22,000.00 120
 
1.4%
Other values (2444) 4695
56.2%
2024-10-13T19:26:39.053554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 43809
52.5%
, 12623
 
15.1%
. 8349
 
10.0%
1 3663
 
4.4%
5 3325
 
4.0%
2 3178
 
3.8%
4 1792
 
2.1%
9 1521
 
1.8%
3 1444
 
1.7%
6 1253
 
1.5%
Other values (2) 2416
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62401
74.8%
Other Punctuation 20972
 
25.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43809
70.2%
1 3663
 
5.9%
5 3325
 
5.3%
2 3178
 
5.1%
4 1792
 
2.9%
9 1521
 
2.4%
3 1444
 
2.3%
6 1253
 
2.0%
8 1232
 
2.0%
7 1184
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 12623
60.2%
. 8349
39.8%

Most occurring scripts

ValueCountFrequency (%)
Common 83373
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43809
52.5%
, 12623
 
15.1%
. 8349
 
10.0%
1 3663
 
4.4%
5 3325
 
4.0%
2 3178
 
3.8%
4 1792
 
2.1%
9 1521
 
1.8%
3 1444
 
1.7%
6 1253
 
1.5%
Other values (2) 2416
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43809
52.5%
, 12623
 
15.1%
. 8349
 
10.0%
1 3663
 
4.4%
5 3325
 
4.0%
2 3178
 
3.8%
4 1792
 
2.1%
9 1521
 
1.8%
3 1444
 
1.7%
6 1253
 
1.5%
Other values (2) 2416
 
2.9%

Standard Unit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size747.2 KiB
KGS
8286 
NOS
 
47
LTR
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25047
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKGS
2nd rowKGS
3rd rowKGS
4th rowKGS
5th rowKGS

Common Values

ValueCountFrequency (%)
KGS 8286
99.2%
NOS 47
 
0.6%
LTR 16
 
0.2%

Length

2024-10-13T19:26:39.198462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T19:26:39.297564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
kgs 8286
99.2%
nos 47
 
0.6%
ltr 16
 
0.2%

Most occurring characters

ValueCountFrequency (%)
S 8333
33.3%
K 8286
33.1%
G 8286
33.1%
N 47
 
0.2%
O 47
 
0.2%
L 16
 
0.1%
T 16
 
0.1%
R 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25047
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 8333
33.3%
K 8286
33.1%
G 8286
33.1%
N 47
 
0.2%
O 47
 
0.2%
L 16
 
0.1%
T 16
 
0.1%
R 16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 25047
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 8333
33.3%
K 8286
33.1%
G 8286
33.1%
N 47
 
0.2%
O 47
 
0.2%
L 16
 
0.1%
T 16
 
0.1%
R 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 8333
33.3%
K 8286
33.1%
G 8286
33.1%
N 47
 
0.2%
O 47
 
0.2%
L 16
 
0.1%
T 16
 
0.1%
R 16
 
0.1%
Distinct700
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size758.0 KiB
2024-10-13T19:26:39.565781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.3202779
Min length4

Characters and Unicode

Total characters36070
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique395 ?
Unique (%)4.7%

Sample

1st row1.34
2nd row1.37
3rd row1.75
4th row1.34
5th row1.25
ValueCountFrequency (%)
1.05 211
 
2.5%
1.15 194
 
2.3%
1.36 181
 
2.2%
1.12 172
 
2.1%
1.35 171
 
2.0%
1.38 168
 
2.0%
1.03 160
 
1.9%
1.17 156
 
1.9%
1.37 155
 
1.9%
1.07 150
 
1.8%
Other values (690) 6631
79.4%
2024-10-13T19:26:39.969204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8576
23.8%
. 8349
23.1%
2 3641
10.1%
0 3118
 
8.6%
3 2581
 
7.2%
4 2271
 
6.3%
5 1665
 
4.6%
9 1478
 
4.1%
7 1369
 
3.8%
8 1227
 
3.4%
Other values (2) 1795
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27139
75.2%
Other Punctuation 8931
 
24.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8576
31.6%
2 3641
13.4%
0 3118
 
11.5%
3 2581
 
9.5%
4 2271
 
8.4%
5 1665
 
6.1%
9 1478
 
5.4%
7 1369
 
5.0%
8 1227
 
4.5%
6 1213
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 8349
93.5%
, 582
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 36070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8576
23.8%
. 8349
23.1%
2 3641
10.1%
0 3118
 
8.6%
3 2581
 
7.2%
4 2271
 
6.3%
5 1665
 
4.6%
9 1478
 
4.1%
7 1369
 
3.8%
8 1227
 
3.4%
Other values (2) 1795
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8576
23.8%
. 8349
23.1%
2 3641
10.1%
0 3118
 
8.6%
3 2581
 
7.2%
4 2271
 
6.3%
5 1665
 
4.6%
9 1478
 
4.1%
7 1369
 
3.8%
8 1227
 
3.4%
Other values (2) 1795
 
5.0%
Distinct822
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size786.1 KiB
2024-10-13T19:26:40.228728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.7635645
Min length4

Characters and Unicode

Total characters64818
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique221 ?
Unique (%)2.6%

Sample

1st row1,343.38
2nd row1,365.00
3rd row1,770.00
4th row1,341.25
5th row1,245.17
ValueCountFrequency (%)
2,500.00 326
 
3.9%
1,380.00 200
 
2.4%
1,050.00 179
 
2.1%
1,080.00 174
 
2.1%
2,335.00 131
 
1.6%
2,300.00 125
 
1.5%
1,200.00 112
 
1.3%
1,100.00 110
 
1.3%
1,400.00 107
 
1.3%
1,175.00 98
 
1.2%
Other values (812) 6787
81.3%
2024-10-13T19:26:40.608303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18166
28.0%
1 9226
14.2%
. 8349
12.9%
, 7559
11.7%
5 4444
 
6.9%
2 3908
 
6.0%
3 3523
 
5.4%
4 2431
 
3.8%
7 1989
 
3.1%
6 1892
 
2.9%
Other values (2) 3331
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48910
75.5%
Other Punctuation 15908
 
24.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18166
37.1%
1 9226
18.9%
5 4444
 
9.1%
2 3908
 
8.0%
3 3523
 
7.2%
4 2431
 
5.0%
7 1989
 
4.1%
6 1892
 
3.9%
8 1775
 
3.6%
9 1556
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 8349
52.5%
, 7559
47.5%

Most occurring scripts

ValueCountFrequency (%)
Common 64818
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18166
28.0%
1 9226
14.2%
. 8349
12.9%
, 7559
11.7%
5 4444
 
6.9%
2 3908
 
6.0%
3 3523
 
5.4%
4 2431
 
3.8%
7 1989
 
3.1%
6 1892
 
2.9%
Other values (2) 3331
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18166
28.0%
1 9226
14.2%
. 8349
12.9%
, 7559
11.7%
5 4444
 
6.9%
2 3908
 
6.0%
3 3523
 
5.4%
4 2431
 
3.8%
7 1989
 
3.1%
6 1892
 
2.9%
Other values (2) 3331
 
5.1%

Unit Rate Currency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size747.5 KiB
USD
7917 
USD
 
203
INR
 
89
AED
 
40
JPY
 
31
Other values (8)
 
69

Length

Max length4
Median length3
Mean length3.027668
Min length3

Characters and Unicode

Total characters25278
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 7917
94.8%
USD 203
 
2.4%
INR 89
 
1.1%
AED 40
 
0.5%
JPY 31
 
0.4%
EUR 18
 
0.2%
INR 17
 
0.2%
GBP 12
 
0.1%
SGD 10
 
0.1%
JPY 5
 
0.1%
Other values (3) 7
 
0.1%

Length

2024-10-13T19:26:40.746245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd 8120
97.3%
inr 106
 
1.3%
aed 43
 
0.5%
jpy 36
 
0.4%
eur 21
 
0.3%
gbp 12
 
0.1%
sgd 10
 
0.1%
chf 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 8173
32.3%
U 8141
32.2%
S 8130
32.2%
231
 
0.9%
R 127
 
0.5%
I 106
 
0.4%
N 106
 
0.4%
E 64
 
0.3%
P 48
 
0.2%
A 43
 
0.2%
Other values (7) 109
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25047
99.1%
Space Separator 231
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 8173
32.6%
U 8141
32.5%
S 8130
32.5%
R 127
 
0.5%
I 106
 
0.4%
N 106
 
0.4%
E 64
 
0.3%
P 48
 
0.2%
A 43
 
0.2%
J 36
 
0.1%
Other values (6) 73
 
0.3%
Space Separator
ValueCountFrequency (%)
231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25047
99.1%
Common 231
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 8173
32.6%
U 8141
32.5%
S 8130
32.5%
R 127
 
0.5%
I 106
 
0.4%
N 106
 
0.4%
E 64
 
0.3%
P 48
 
0.2%
A 43
 
0.2%
J 36
 
0.1%
Other values (6) 73
 
0.3%
Common
ValueCountFrequency (%)
231
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 8173
32.3%
U 8141
32.2%
S 8130
32.2%
231
 
0.9%
R 127
 
0.5%
I 106
 
0.4%
N 106
 
0.4%
E 64
 
0.3%
P 48
 
0.2%
A 43
 
0.2%
Other values (7) 109
 
0.4%

Port of Destination
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size772.2 KiB
Kandla
4493 
Hazira
1642 
Jnpt
739 
JNPT
620 
Bombay Sea
 
289
Other values (23)
566 

Length

Max length18
Median length6
Mean length6.0565337
Min length4

Characters and Unicode

Total characters50566
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowBombay Sea
2nd rowHazira
3rd rowHazira
4th rowHazira
5th rowHazira

Common Values

ValueCountFrequency (%)
Kandla 4493
53.8%
Hazira 1642
 
19.7%
Jnpt 739
 
8.9%
JNPT 620
 
7.4%
Bombay Sea 289
 
3.5%
Ennore 150
 
1.8%
Bombay Air 89
 
1.1%
Calcutta Sea 81
 
1.0%
Bangalore Air 62
 
0.7%
Mundrasez 42
 
0.5%
Other values (18) 142
 
1.7%

Length

2024-10-13T19:26:40.870054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kandla 4493
50.2%
hazira 1642
 
18.3%
jnpt 1359
 
15.2%
sea 416
 
4.6%
bombay 378
 
4.2%
air 184
 
2.1%
ennore 150
 
1.7%
calcutta 81
 
0.9%
bangalore 64
 
0.7%
madras 49
 
0.5%
Other values (20) 140
 
1.6%

Most occurring characters

ValueCountFrequency (%)
a 13621
26.9%
n 5692
11.3%
d 4696
 
9.3%
l 4657
 
9.2%
K 4494
 
8.9%
r 2212
 
4.4%
i 1866
 
3.7%
z 1692
 
3.3%
H 1657
 
3.3%
J 1359
 
2.7%
Other values (31) 8620
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39014
77.2%
Uppercase Letter 10912
 
21.6%
Space Separator 607
 
1.2%
Dash Punctuation 33
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13621
34.9%
n 5692
14.6%
d 4696
 
12.0%
l 4657
 
11.9%
r 2212
 
5.7%
i 1866
 
4.8%
z 1692
 
4.3%
t 903
 
2.3%
p 742
 
1.9%
e 710
 
1.8%
Other values (11) 2223
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
K 4494
41.2%
H 1657
 
15.2%
J 1359
 
12.5%
N 632
 
5.8%
P 622
 
5.7%
T 620
 
5.7%
B 443
 
4.1%
S 440
 
4.0%
A 188
 
1.7%
E 172
 
1.6%
Other values (8) 285
 
2.6%
Space Separator
ValueCountFrequency (%)
607
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49926
98.7%
Common 640
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13621
27.3%
n 5692
11.4%
d 4696
 
9.4%
l 4657
 
9.3%
K 4494
 
9.0%
r 2212
 
4.4%
i 1866
 
3.7%
z 1692
 
3.4%
H 1657
 
3.3%
J 1359
 
2.7%
Other values (29) 7980
16.0%
Common
ValueCountFrequency (%)
607
94.8%
- 33
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13621
26.9%
n 5692
11.3%
d 4696
 
9.3%
l 4657
 
9.2%
K 4494
 
8.9%
r 2212
 
4.4%
i 1866
 
3.7%
z 1692
 
3.3%
H 1657
 
3.3%
J 1359
 
2.7%
Other values (31) 8620
17.0%

Shipment Mode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size747.2 KiB
Sea
8097 
Air
 
184
Sez
 
60
SEZ
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25047
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSea
2nd rowSea
3rd rowSea
4th rowSea
5th rowSea

Common Values

ValueCountFrequency (%)
Sea 8097
97.0%
Air 184
 
2.2%
Sez 60
 
0.7%
SEZ 8
 
0.1%

Length

2024-10-13T19:26:40.986229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T19:26:41.086006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sea 8097
97.0%
air 184
 
2.2%
sez 68
 
0.8%

Most occurring characters

ValueCountFrequency (%)
S 8165
32.6%
e 8157
32.6%
a 8097
32.3%
A 184
 
0.7%
i 184
 
0.7%
r 184
 
0.7%
z 60
 
0.2%
E 8
 
< 0.1%
Z 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16682
66.6%
Uppercase Letter 8365
33.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8157
48.9%
a 8097
48.5%
i 184
 
1.1%
r 184
 
1.1%
z 60
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
S 8165
97.6%
A 184
 
2.2%
E 8
 
0.1%
Z 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 25047
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 8165
32.6%
e 8157
32.6%
a 8097
32.3%
A 184
 
0.7%
i 184
 
0.7%
r 184
 
0.7%
z 60
 
0.2%
E 8
 
< 0.1%
Z 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 8165
32.6%
e 8157
32.6%
a 8097
32.3%
A 184
 
0.7%
i 184
 
0.7%
r 184
 
0.7%
z 60
 
0.2%
E 8
 
< 0.1%
Z 8
 
< 0.1%

Country of Origin
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size792.9 KiB
Singapore
3244 
Saudi Arabia
1313 
China
1093 
Kuwait
980 
South Korea
685 
Other values (24)
1034 

Length

Max length20
Median length14
Mean length8.5952809
Min length4

Characters and Unicode

Total characters71762
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowSaudi Arabia
2nd rowSingapore
3rd rowSaudi Arabia
4th rowSaudi Arabia
5th rowSingapore

Common Values

ValueCountFrequency (%)
Singapore 3244
38.9%
Saudi Arabia 1313
15.7%
China 1093
 
13.1%
Kuwait 980
 
11.7%
South Korea 685
 
8.2%
Malaysia 386
 
4.6%
Taiwan 237
 
2.8%
United States 84
 
1.0%
Germany 80
 
1.0%
Thailand 72
 
0.9%
Other values (19) 175
 
2.1%

Length

2024-10-13T19:26:41.223071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
singapore 3244
31.0%
saudi 1313
12.6%
arabia 1313
12.6%
china 1093
 
10.5%
kuwait 980
 
9.4%
south 685
 
6.6%
korea 685
 
6.6%
malaysia 386
 
3.7%
taiwan 237
 
2.3%
united 101
 
1.0%
Other values (23) 412
 
3.9%

Most occurring characters

ValueCountFrequency (%)
a 12079
16.8%
i 8790
12.2%
r 5411
 
7.5%
S 5342
 
7.4%
n 4980
 
6.9%
o 4632
 
6.5%
e 4263
 
5.9%
p 3293
 
4.6%
g 3266
 
4.6%
u 2986
 
4.2%
Other values (28) 16720
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59213
82.5%
Uppercase Letter 10449
 
14.6%
Space Separator 2100
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12079
20.4%
i 8790
14.8%
r 5411
9.1%
n 4980
8.4%
o 4632
 
7.8%
e 4263
 
7.2%
p 3293
 
5.6%
g 3266
 
5.5%
u 2986
 
5.0%
t 1967
 
3.3%
Other values (10) 7546
12.7%
Uppercase Letter
ValueCountFrequency (%)
S 5342
51.1%
K 1681
 
16.1%
A 1314
 
12.6%
C 1094
 
10.5%
M 386
 
3.7%
T 309
 
3.0%
U 101
 
1.0%
G 80
 
0.8%
I 49
 
0.5%
J 40
 
0.4%
Other values (7) 53
 
0.5%
Space Separator
ValueCountFrequency (%)
2100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69662
97.1%
Common 2100
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12079
17.3%
i 8790
12.6%
r 5411
 
7.8%
S 5342
 
7.7%
n 4980
 
7.1%
o 4632
 
6.6%
e 4263
 
6.1%
p 3293
 
4.7%
g 3266
 
4.7%
u 2986
 
4.3%
Other values (27) 14620
21.0%
Common
ValueCountFrequency (%)
2100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12079
16.8%
i 8790
12.2%
r 5411
 
7.5%
S 5342
 
7.4%
n 4980
 
6.9%
o 4632
 
6.5%
e 4263
 
5.9%
p 3293
 
4.6%
g 3266
 
4.6%
u 2986
 
4.2%
Other values (28) 16720
23.3%

Port Of Origin
Text

MISSING 

Distinct72
Distinct (%)1.2%
Missing2222
Missing (%)26.6%
Memory size739.7 KiB
2024-10-13T19:26:41.383927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length28
Median length17
Mean length8.9934715
Min length2

Characters and Unicode

Total characters55103
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.4%

Sample

1st rowNa
2nd rowNa
3rd rowNa
4th rowNa
5th rowNa
ValueCountFrequency (%)
zzz-unknown 1515
19.5%
op 1231
15.8%
singapore 1231
15.8%
shuaiba 784
10.1%
jubail 761
9.8%
na 649
8.4%
daesanseosan 273
 
3.5%
jingjiang 192
 
2.5%
johor 165
 
2.1%
mailiao 158
 
2.0%
Other values (78) 809
10.4%
2024-10-13T19:26:41.667641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 7119
 
12.9%
a 6228
 
11.3%
Z 4552
 
8.3%
i 3815
 
6.9%
o 3704
 
6.7%
S 2045
 
3.7%
e 2025
 
3.7%
g 1869
 
3.4%
u 1809
 
3.3%
r 1795
 
3.3%
Other values (41) 20142
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38258
69.4%
Uppercase Letter 13544
 
24.6%
Space Separator 1641
 
3.0%
Dash Punctuation 1515
 
2.7%
Other Punctuation 145
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 7119
18.6%
a 6228
16.3%
i 3815
10.0%
o 3704
9.7%
e 2025
 
5.3%
g 1869
 
4.9%
u 1809
 
4.7%
r 1795
 
4.7%
b 1574
 
4.1%
k 1574
 
4.1%
Other values (15) 6746
17.6%
Uppercase Letter
ValueCountFrequency (%)
Z 4552
33.6%
S 2045
15.1%
U 1523
 
11.2%
P 1373
 
10.1%
O 1231
 
9.1%
J 1145
 
8.5%
N 674
 
5.0%
D 274
 
2.0%
M 211
 
1.6%
G 149
 
1.1%
Other values (11) 367
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 137
94.5%
. 4
 
2.8%
/ 4
 
2.8%
Space Separator
ValueCountFrequency (%)
1641
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1515
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51802
94.0%
Common 3301
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 7119
13.7%
a 6228
 
12.0%
Z 4552
 
8.8%
i 3815
 
7.4%
o 3704
 
7.2%
S 2045
 
3.9%
e 2025
 
3.9%
g 1869
 
3.6%
u 1809
 
3.5%
r 1795
 
3.5%
Other values (36) 16841
32.5%
Common
ValueCountFrequency (%)
1641
49.7%
- 1515
45.9%
, 137
 
4.2%
. 4
 
0.1%
/ 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 7119
 
12.9%
a 6228
 
11.3%
Z 4552
 
8.3%
i 3815
 
6.9%
o 3704
 
6.7%
S 2045
 
3.7%
e 2025
 
3.7%
g 1869
 
3.4%
u 1809
 
3.3%
r 1795
 
3.3%
Other values (41) 20142
36.6%
Distinct652
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size758.0 KiB
2024-10-13T19:26:41.925511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.3188406
Min length4

Characters and Unicode

Total characters36058
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique379 ?
Unique (%)4.5%

Sample

1st row1.34
2nd row1.37
3rd row1.75
4th row1.34
5th row1.25
ValueCountFrequency (%)
1.05 256
 
3.1%
1.07 227
 
2.7%
1.15 212
 
2.5%
1.12 197
 
2.4%
1.03 190
 
2.3%
1.36 167
 
2.0%
1.08 162
 
1.9%
1.17 161
 
1.9%
1.02 154
 
1.8%
1.04 152
 
1.8%
Other values (642) 6471
77.5%
2024-10-13T19:26:42.326751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8540
23.7%
. 8349
23.2%
2 3678
10.2%
0 3421
9.5%
3 2535
 
7.0%
4 2193
 
6.1%
5 1705
 
4.7%
9 1464
 
4.1%
7 1237
 
3.4%
8 1189
 
3.3%
Other values (2) 1747
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27101
75.2%
Other Punctuation 8957
 
24.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8540
31.5%
2 3678
13.6%
0 3421
12.6%
3 2535
 
9.4%
4 2193
 
8.1%
5 1705
 
6.3%
9 1464
 
5.4%
7 1237
 
4.6%
8 1189
 
4.4%
6 1139
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 8349
93.2%
, 608
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 36058
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8540
23.7%
. 8349
23.2%
2 3678
10.2%
0 3421
9.5%
3 2535
 
7.0%
4 2193
 
6.1%
5 1705
 
4.7%
9 1464
 
4.1%
7 1237
 
3.4%
8 1189
 
3.3%
Other values (2) 1747
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8540
23.7%
. 8349
23.2%
2 3678
10.2%
0 3421
9.5%
3 2535
 
7.0%
4 2193
 
6.1%
5 1705
 
4.7%
9 1464
 
4.1%
7 1237
 
3.4%
8 1189
 
3.3%
Other values (2) 1747
 
4.8%

Month
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size788.0 KiB
Mar-2023
825 
Jun-2022
768 
Jul-2022
756 
Nov-2022
718 
Jan-2023
702 
Other values (7)
4580 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters66792
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApr-2022
2nd rowApr-2022
3rd rowApr-2022
4th rowApr-2022
5th rowApr-2022

Common Values

ValueCountFrequency (%)
Mar-2023 825
9.9%
Jun-2022 768
9.2%
Jul-2022 756
9.1%
Nov-2022 718
8.6%
Jan-2023 702
8.4%
Aug-2022 694
8.3%
Dec-2022 685
8.2%
Feb-2023 669
8.0%
Apr-2022 654
7.8%
May-2022 650
7.8%
Other values (2) 1228
14.7%

Length

2024-10-13T19:26:42.452525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar-2023 825
9.9%
jun-2022 768
9.2%
jul-2022 756
9.1%
nov-2022 718
8.6%
jan-2023 702
8.4%
aug-2022 694
8.3%
dec-2022 685
8.2%
feb-2023 669
8.0%
apr-2022 654
7.8%
may-2022 650
7.8%
Other values (2) 1228
14.7%

Most occurring characters

ValueCountFrequency (%)
2 22851
34.2%
- 8349
 
12.5%
0 8349
 
12.5%
J 2226
 
3.3%
u 2218
 
3.3%
3 2196
 
3.3%
a 2177
 
3.3%
e 1984
 
3.0%
r 1479
 
2.2%
M 1475
 
2.2%
Other values (16) 13488
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33396
50.0%
Lowercase Letter 16698
25.0%
Dash Punctuation 8349
 
12.5%
Uppercase Letter 8349
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 2218
13.3%
a 2177
13.0%
e 1984
11.9%
r 1479
8.9%
n 1470
8.8%
p 1284
7.7%
c 1283
7.7%
l 756
 
4.5%
v 718
 
4.3%
o 718
 
4.3%
Other values (4) 2611
15.6%
Uppercase Letter
ValueCountFrequency (%)
J 2226
26.7%
M 1475
17.7%
A 1348
16.1%
N 718
 
8.6%
D 685
 
8.2%
F 669
 
8.0%
S 630
 
7.5%
O 598
 
7.2%
Decimal Number
ValueCountFrequency (%)
2 22851
68.4%
0 8349
 
25.0%
3 2196
 
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 8349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41745
62.5%
Latin 25047
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 2226
 
8.9%
u 2218
 
8.9%
a 2177
 
8.7%
e 1984
 
7.9%
r 1479
 
5.9%
M 1475
 
5.9%
n 1470
 
5.9%
A 1348
 
5.4%
p 1284
 
5.1%
c 1283
 
5.1%
Other values (12) 8103
32.4%
Common
ValueCountFrequency (%)
2 22851
54.7%
- 8349
 
20.0%
0 8349
 
20.0%
3 2196
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 22851
34.2%
- 8349
 
12.5%
0 8349
 
12.5%
J 2226
 
3.3%
u 2218
 
3.3%
3 2196
 
3.3%
a 2177
 
3.3%
e 1984
 
3.0%
r 1479
 
2.2%
M 1475
 
2.2%
Other values (16) 13488
20.2%

Record Id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8349
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62817553
Minimum155596
Maximum1.1584406 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.5 KiB
2024-10-13T19:26:42.569552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum155596
5-th percentile6092111.8
Q123667365
median77607338
Q391442358
95-th percentile1.1357875 × 108
Maximum1.1584406 × 108
Range1.1568846 × 108
Interquartile range (IQR)67774993

Descriptive statistics

Standard deviation37082287
Coefficient of variation (CV)0.59031728
Kurtosis-1.4947367
Mean62817553
Median Absolute Deviation (MAD)30168187
Skewness-0.25250438
Sum5.2446375 × 1011
Variance1.375096 × 1015
MonotonicityNot monotonic
2024-10-13T19:26:42.925255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29611604 1
 
< 0.1%
115588755 1
 
< 0.1%
115588745 1
 
< 0.1%
115588843 1
 
< 0.1%
115589094 1
 
< 0.1%
115588801 1
 
< 0.1%
115588799 1
 
< 0.1%
115588747 1
 
< 0.1%
115588784 1
 
< 0.1%
115588750 1
 
< 0.1%
Other values (8339) 8339
99.9%
ValueCountFrequency (%)
155596 1
< 0.1%
168158 1
< 0.1%
276707 1
< 0.1%
286555 1
< 0.1%
286661 1
< 0.1%
297361 1
< 0.1%
347464 1
< 0.1%
354425 1
< 0.1%
382689 1
< 0.1%
417443 1
< 0.1%
ValueCountFrequency (%)
115844056 1
< 0.1%
115844055 1
< 0.1%
115844049 1
< 0.1%
115844035 1
< 0.1%
115844033 1
< 0.1%
115844032 1
< 0.1%
115844021 1
< 0.1%
115844006 1
< 0.1%
115843987 1
< 0.1%
115843986 1
< 0.1%
Distinct199
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size924.8 KiB
2024-10-13T19:26:43.139505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length115
Median length47
Mean length24.777937
Min length8

Characters and Unicode

Total characters206871
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.7%

Sample

1st rowSUPREME PETROCHEM LTD
2nd rowSHIVA PERFORMANCE MATERIALS PRIVATE LIMITED
3rd rowJESONS INDUSTRIES LIMITED
4th rowINEOS STYROLUTION INDIA LIMITED
5th rowJESONS INDUSTRIES LIMITED
ValueCountFrequency (%)
ltd 3759
 
12.2%
limited 3055
 
9.9%
pvt 1811
 
5.9%
industries 1286
 
4.2%
chemicals 1157
 
3.7%
india 999
 
3.2%
visen 976
 
3.2%
private 878
 
2.8%
polymers 674
 
2.2%
petrochem 590
 
1.9%
Other values (325) 15703
50.8%
2024-10-13T19:26:43.495330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22539
 
10.9%
I 22379
 
10.8%
E 19098
 
9.2%
T 14827
 
7.2%
L 13558
 
6.6%
S 12897
 
6.2%
A 12041
 
5.8%
D 11455
 
5.5%
R 10758
 
5.2%
N 10228
 
4.9%
Other values (43) 57091
27.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 184048
89.0%
Space Separator 22539
 
10.9%
Decimal Number 140
 
0.1%
Lowercase Letter 138
 
0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 22379
12.2%
E 19098
10.4%
T 14827
 
8.1%
L 13558
 
7.4%
S 12897
 
7.0%
A 12041
 
6.5%
D 11455
 
6.2%
R 10758
 
5.8%
N 10228
 
5.6%
C 9731
 
5.3%
Other values (16) 47076
25.6%
Lowercase Letter
ValueCountFrequency (%)
t 20
14.5%
n 18
13.0%
r 14
10.1%
i 14
10.1%
a 10
7.2%
e 8
 
5.8%
y 8
 
5.8%
g 8
 
5.8%
d 8
 
5.8%
v 6
 
4.3%
Other values (6) 24
17.4%
Decimal Number
ValueCountFrequency (%)
0 46
32.9%
1 45
32.1%
5 40
28.6%
2 2
 
1.4%
3 2
 
1.4%
4 2
 
1.4%
7 1
 
0.7%
8 1
 
0.7%
6 1
 
0.7%
Space Separator
ValueCountFrequency (%)
22539
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 184186
89.0%
Common 22685
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 22379
12.2%
E 19098
10.4%
T 14827
 
8.1%
L 13558
 
7.4%
S 12897
 
7.0%
A 12041
 
6.5%
D 11455
 
6.2%
R 10758
 
5.8%
N 10228
 
5.6%
C 9731
 
5.3%
Other values (32) 47214
25.6%
Common
ValueCountFrequency (%)
22539
99.4%
0 46
 
0.2%
1 45
 
0.2%
5 40
 
0.2%
- 6
 
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206871
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22539
 
10.9%
I 22379
 
10.8%
E 19098
 
9.2%
T 14827
 
7.2%
L 13558
 
6.6%
S 12897
 
6.2%
A 12041
 
5.8%
D 11455
 
5.5%
R 10758
 
5.2%
N 10228
 
4.9%
Other values (43) 57091
27.6%
Distinct209
Distinct (%)2.5%
Missing2
Missing (%)< 0.1%
Memory size935.9 KiB
2024-10-13T19:26:43.751052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length50
Median length42
Mean length26.148317
Min length8

Characters and Unicode

Total characters218260
Distinct characters47
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)0.7%

Sample

1st rowSABIC ASIA PACIFIC PTE LTD
2nd rowM S SHELL INTERNATIONAL EASTERN TRA
3rd rowSIPCHEM MARKETING COMPANY
4th rowM S SABIC ASIA PACIFIC PTE LTD
5th rowSHELL INTERNATIONAL EASTERN TRADING
ValueCountFrequency (%)
ltd 3578
 
10.0%
international 2441
 
6.8%
pte 2367
 
6.6%
s 2350
 
6.6%
m 2149
 
6.0%
company 1753
 
4.9%
shell 1477
 
4.1%
eastern 1461
 
4.1%
trading 1230
 
3.4%
marketing 980
 
2.7%
Other values (275) 15892
44.5%
2024-10-13T19:26:44.143544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27401
12.6%
E 20523
9.4%
A 18814
 
8.6%
T 18542
 
8.5%
N 18062
 
8.3%
I 15258
 
7.0%
L 13692
 
6.3%
R 12386
 
5.7%
C 11686
 
5.4%
S 10838
 
5.0%
Other values (37) 51058
23.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 189177
86.7%
Space Separator 27401
 
12.6%
Lowercase Letter 1629
 
0.7%
Dash Punctuation 47
 
< 0.1%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 20523
10.8%
A 18814
9.9%
T 18542
9.8%
N 18062
9.5%
I 15258
 
8.1%
L 13692
 
7.2%
R 12386
 
6.5%
C 11686
 
6.2%
S 10838
 
5.7%
O 8795
 
4.6%
Other values (15) 40581
21.5%
Lowercase Letter
ValueCountFrequency (%)
e 209
12.8%
a 169
10.4%
i 165
10.1%
n 163
10.0%
m 123
 
7.6%
p 118
 
7.2%
c 111
 
6.8%
r 100
 
6.1%
k 80
 
4.9%
t 79
 
4.8%
Other values (9) 312
19.2%
Space Separator
ValueCountFrequency (%)
27401
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%
Decimal Number
ValueCountFrequency (%)
7 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 190806
87.4%
Common 27454
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 20523
10.8%
A 18814
9.9%
T 18542
9.7%
N 18062
9.5%
I 15258
 
8.0%
L 13692
 
7.2%
R 12386
 
6.5%
C 11686
 
6.1%
S 10838
 
5.7%
O 8795
 
4.6%
Other values (34) 42210
22.1%
Common
ValueCountFrequency (%)
27401
99.8%
- 47
 
0.2%
7 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 218260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27401
12.6%
E 20523
9.4%
A 18814
 
8.6%
T 18542
 
8.5%
N 18062
 
8.3%
I 15258
 
7.0%
L 13692
 
6.3%
R 12386
 
5.7%
C 11686
 
5.4%
S 10838
 
5.0%
Other values (37) 51058
23.4%
Distinct183
Distinct (%)2.4%
Missing575
Missing (%)6.9%
Memory size1.1 MiB
2024-10-13T19:26:44.344769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length132
Median length104
Mean length60.626833
Min length18

Characters and Unicode

Total characters471313
Distinct characters71
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)0.7%

Sample

1st rowSOLITAIRE CORPORATE PARK,BLDG 11 5 TH FLR GURU HARGOVINJI MARG CHAKALAANDHERI WEST
2nd row904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WEST
3rd row5TH FLOOR OHM HOUSE-II,,OHM BUSINE SS PARK, SUBHANPURA,
4th row904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WEST
5th row5TH FLOOR OHM HOUSE-II,,OHM BUSINE SS PARK, SUBHANPURA,
ValueCountFrequency (%)
3653
 
5.2%
andheri 2152
 
3.1%
floor 1602
 
2.3%
no 1512
 
2.1%
point 1349
 
1.9%
plot 1238
 
1.8%
road 1103
 
1.6%
western 1034
 
1.5%
west 1025
 
1.5%
w 1013
 
1.4%
Other values (907) 54785
77.7%
2024-10-13T19:26:44.692816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
62700
 
13.3%
A 41795
 
8.9%
, 29276
 
6.2%
R 28431
 
6.0%
N 27100
 
5.7%
O 25244
 
5.4%
E 24231
 
5.1%
I 20868
 
4.4%
T 20564
 
4.4%
H 16260
 
3.4%
Other values (61) 174844
37.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 330085
70.0%
Space Separator 62700
 
13.3%
Other Punctuation 41228
 
8.7%
Decimal Number 31327
 
6.6%
Dash Punctuation 2570
 
0.5%
Open Punctuation 1343
 
0.3%
Close Punctuation 1343
 
0.3%
Lowercase Letter 650
 
0.1%
Control 30
 
< 0.1%
Modifier Symbol 29
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 41795
12.7%
R 28431
 
8.6%
N 27100
 
8.2%
O 25244
 
7.6%
E 24231
 
7.3%
I 20868
 
6.3%
T 20564
 
6.2%
H 16260
 
4.9%
S 15869
 
4.8%
L 15333
 
4.6%
Other values (16) 94390
28.6%
Lowercase Letter
ValueCountFrequency (%)
n 104
16.0%
s 102
15.7%
b 90
13.8%
p 84
12.9%
a 80
12.3%
r 30
 
4.6%
h 30
 
4.6%
e 24
 
3.7%
i 24
 
3.7%
d 20
 
3.1%
Other values (8) 62
9.5%
Decimal Number
ValueCountFrequency (%)
1 6322
20.2%
5 5774
18.4%
0 4535
14.5%
4 3421
10.9%
2 2895
9.2%
7 2171
 
6.9%
3 1928
 
6.2%
8 1738
 
5.5%
9 1416
 
4.5%
6 1127
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 29276
71.0%
. 8612
 
20.9%
/ 1638
 
4.0%
& 1096
 
2.7%
* 512
 
1.2%
; 84
 
0.2%
: 10
 
< 0.1%
Control
ValueCountFrequency (%)
26
86.7%
€ 2
 
6.7%
“ 2
 
6.7%
Math Symbol
ValueCountFrequency (%)
| 7
87.5%
+ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
62700
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2570
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1343
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1343
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 330735
70.2%
Common 140578
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 41795
12.6%
R 28431
 
8.6%
N 27100
 
8.2%
O 25244
 
7.6%
E 24231
 
7.3%
I 20868
 
6.3%
T 20564
 
6.2%
H 16260
 
4.9%
S 15869
 
4.8%
L 15333
 
4.6%
Other values (34) 95040
28.7%
Common
ValueCountFrequency (%)
62700
44.6%
, 29276
20.8%
. 8612
 
6.1%
1 6322
 
4.5%
5 5774
 
4.1%
0 4535
 
3.2%
4 3421
 
2.4%
2 2895
 
2.1%
- 2570
 
1.8%
7 2171
 
1.5%
Other values (17) 12302
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 471307
> 99.9%
None 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62700
 
13.3%
A 41795
 
8.9%
, 29276
 
6.2%
R 28431
 
6.0%
N 27100
 
5.7%
O 25244
 
5.4%
E 24231
 
5.1%
I 20868
 
4.4%
T 20564
 
4.4%
H 16260
 
3.4%
Other values (58) 174838
37.1%
None
ValueCountFrequency (%)
â 2
33.3%
€ 2
33.3%
“ 2
33.3%

Importer City- Unified
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct44
Distinct (%)0.6%
Missing742
Missing (%)8.9%
Memory size774.7 KiB
MUMBAI
3778 
Mumbai
2049 
KOLKATA
 
365
VADODARA
 
274
New Delhi
 
229
Other values (39)
912 

Length

Max length22
Median length6
Mean length6.3066912
Min length4

Characters and Unicode

Total characters47975
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowMUMBAI
2nd rowMUMBAI
3rd rowVADODARA
4th rowMUMBAI
5th rowVADODARA

Common Values

ValueCountFrequency (%)
MUMBAI 3778
45.3%
Mumbai 2049
24.5%
KOLKATA 365
 
4.4%
VADODARA 274
 
3.3%
New Delhi 229
 
2.7%
Delhi 200
 
2.4%
FARIDABAD 121
 
1.4%
NOIDA 70
 
0.8%
DELHI 64
 
0.8%
BANGALORE 63
 
0.8%
Other values (34) 394
 
4.7%
(Missing) 742
 
8.9%

Length

2024-10-13T19:26:44.834763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai 5827
74.2%
delhi 493
 
6.3%
kolkata 377
 
4.8%
vadodara 274
 
3.5%
new 229
 
2.9%
faridabad 121
 
1.5%
noida 70
 
0.9%
bangalore 65
 
0.8%
kanpur 62
 
0.8%
ludhiana 41
 
0.5%
Other values (32) 297
 
3.8%

Most occurring characters

ValueCountFrequency (%)
M 9703
20.2%
A 6198
12.9%
I 4141
8.6%
B 4052
8.4%
U 3816
 
8.0%
i 2533
 
5.3%
a 2305
 
4.8%
u 2190
 
4.6%
b 2069
 
4.3%
m 2051
 
4.3%
Other values (35) 8917
18.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 34145
71.2%
Lowercase Letter 13567
 
28.3%
Space Separator 257
 
0.5%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 9703
28.4%
A 6198
18.2%
I 4141
12.1%
B 4052
11.9%
U 3816
 
11.2%
D 1477
 
4.3%
O 846
 
2.5%
K 845
 
2.5%
R 559
 
1.6%
L 541
 
1.6%
Other values (12) 1967
 
5.8%
Lowercase Letter
ValueCountFrequency (%)
i 2533
18.7%
a 2305
17.0%
u 2190
16.1%
b 2069
15.3%
m 2051
15.1%
e 697
 
5.1%
h 486
 
3.6%
l 445
 
3.3%
w 229
 
1.7%
r 145
 
1.1%
Other values (10) 417
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 4
66.7%
, 2
33.3%
Space Separator
ValueCountFrequency (%)
257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47712
99.5%
Common 263
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 9703
20.3%
A 6198
13.0%
I 4141
8.7%
B 4052
8.5%
U 3816
 
8.0%
i 2533
 
5.3%
a 2305
 
4.8%
u 2190
 
4.6%
b 2069
 
4.3%
m 2051
 
4.3%
Other values (32) 8654
18.1%
Common
ValueCountFrequency (%)
257
97.7%
. 4
 
1.5%
, 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 9703
20.2%
A 6198
12.9%
I 4141
8.6%
B 4052
8.4%
U 3816
 
8.0%
i 2533
 
5.3%
a 2305
 
4.8%
u 2190
 
4.6%
b 2069
 
4.3%
m 2051
 
4.3%
Other values (35) 8917
18.6%

Importer Pincode- Unified
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct101
Distinct (%)1.2%
Missing137
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean361875.51
Minimum0
Maximum700091
Zeros557
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size388.5 KiB
2024-10-13T19:26:44.961417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1400009
median400051
Q3400066
95-th percentile600040
Maximum700091
Range700091
Interquartile range (IQR)57

Descriptive statistics

Standard deviation146493.91
Coefficient of variation (CV)0.40481851
Kurtosis1.6095963
Mean361875.51
Median Absolute Deviation (MAD)40
Skewness-0.81393197
Sum2.9717217 × 109
Variance2.1460464 × 1010
MonotonicityNot monotonic
2024-10-13T19:26:45.100655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400021 1348
16.1%
400058 1287
15.4%
400093 1063
12.7%
400066 577
 
6.9%
0 557
 
6.7%
400009 325
 
3.9%
700017 282
 
3.4%
390023 240
 
2.9%
400059 239
 
2.9%
400011 219
 
2.6%
Other values (91) 2075
24.9%
ValueCountFrequency (%)
0 557
6.7%
110001 73
 
0.9%
110002 138
 
1.7%
110005 9
 
0.1%
110020 94
 
1.1%
110028 11
 
0.1%
110033 70
 
0.8%
110034 28
 
0.3%
110035 5
 
0.1%
110057 1
 
< 0.1%
ValueCountFrequency (%)
700091 81
 
1.0%
700071 12
 
0.1%
700017 282
3.4%
700001 2
 
< 0.1%
682310 3
 
< 0.1%
603105 7
 
0.1%
600119 16
 
0.2%
600040 14
 
0.2%
600016 9
 
0.1%
562106 16
 
0.2%

Importer State- Unified
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct24
Distinct (%)0.3%
Missing692
Missing (%)8.3%
Memory size804.7 KiB
MAHARASHTRA
5811 
WEST BENGAL
 
365
GUJARAT
 
344
DELHI
 
264
Delhi
 
229
Other values (19)
644 

Length

Max length14
Median length11
Mean length10.326237
Min length5

Characters and Unicode

Total characters79068
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMAHARASHTRA
2nd rowMAHARASHTRA
3rd rowGUJARAT
4th rowMAHARASHTRA
5th rowGUJARAT

Common Values

ValueCountFrequency (%)
MAHARASHTRA 5811
69.6%
WEST BENGAL 365
 
4.4%
GUJARAT 344
 
4.1%
DELHI 264
 
3.2%
Delhi 229
 
2.7%
UTTAR PRADESH 145
 
1.7%
Maharashtra 131
 
1.6%
HARYANA 121
 
1.4%
KARNATAKA 67
 
0.8%
TAMIL NADU 46
 
0.6%
Other values (14) 134
 
1.6%
(Missing) 692
 
8.3%

Length

2024-10-13T19:26:45.232222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maharashtra 5942
72.2%
delhi 493
 
6.0%
bengal 377
 
4.6%
west 377
 
4.6%
gujarat 358
 
4.3%
pradesh 151
 
1.8%
uttar 145
 
1.8%
haryana 121
 
1.5%
karnataka 69
 
0.8%
punjab 67
 
0.8%
Other values (8) 131
 
1.6%

Most occurring characters

ValueCountFrequency (%)
A 25447
32.2%
R 12467
15.8%
H 12172
15.4%
T 6949
 
8.8%
S 6335
 
8.0%
M 5990
 
7.6%
E 1160
 
1.5%
G 738
 
0.9%
D 693
 
0.9%
L 692
 
0.9%
Other values (26) 6425
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 75890
96.0%
Lowercase Letter 2604
 
3.3%
Space Separator 574
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 25447
33.5%
R 12467
16.4%
H 12172
16.0%
T 6949
 
9.2%
S 6335
 
8.3%
M 5990
 
7.9%
E 1160
 
1.5%
G 738
 
1.0%
D 693
 
0.9%
L 692
 
0.9%
Other values (10) 3247
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
a 615
23.6%
h 498
19.1%
r 282
10.8%
e 257
9.9%
l 243
 
9.3%
i 229
 
8.8%
t 162
 
6.2%
s 148
 
5.7%
n 47
 
1.8%
j 41
 
1.6%
Other values (5) 82
 
3.1%
Space Separator
ValueCountFrequency (%)
574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78494
99.3%
Common 574
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 25447
32.4%
R 12467
15.9%
H 12172
15.5%
T 6949
 
8.9%
S 6335
 
8.1%
M 5990
 
7.6%
E 1160
 
1.5%
G 738
 
0.9%
D 693
 
0.9%
L 692
 
0.9%
Other values (25) 5851
 
7.5%
Common
ValueCountFrequency (%)
574
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 25447
32.2%
R 12467
15.8%
H 12172
15.4%
T 6949
 
8.8%
S 6335
 
8.0%
M 5990
 
7.6%
E 1160
 
1.5%
G 738
 
0.9%
D 693
 
0.9%
L 692
 
0.9%
Other values (26) 6425
 
8.1%
Distinct209
Distinct (%)2.6%
Missing411
Missing (%)4.9%
Memory size963.2 KiB
2024-10-13T19:26:45.404511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length70
Median length61
Mean length31.892542
Min length1

Characters and Unicode

Total characters253163
Distinct characters26
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)0.7%

Sample

1st row919820037286
2nd row919819883380
3rd row919076307694
4th row919819883380
5th row919076307694
ValueCountFrequency (%)
022-66443333 936
 
11.3%
28357532 510
 
6.2%
917666986831 497
 
6.0%
65102323 475
 
5.8%
919820037286 387
 
4.7%
919870423455 364
 
4.4%
919773333523 351
 
4.3%
919322884854 315
 
3.8%
0 293
 
3.6%
919820689243 287
 
3.5%
Other values (170) 3836
46.5%
2024-10-13T19:26:45.746741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
161419
63.8%
3 13772
 
5.4%
9 12293
 
4.9%
2 11162
 
4.4%
8 10525
 
4.2%
1 9108
 
3.6%
6 8378
 
3.3%
0 7266
 
2.9%
4 6114
 
2.4%
7 5599
 
2.2%
Other values (16) 7527
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 161419
63.8%
Decimal Number 89373
35.3%
Dash Punctuation 1368
 
0.5%
Lowercase Letter 545
 
0.2%
Other Punctuation 274
 
0.1%
Uppercase Letter 184
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 13772
15.4%
9 12293
13.8%
2 11162
12.5%
8 10525
11.8%
1 9108
10.2%
6 8378
9.4%
0 7266
8.1%
4 6114
6.8%
7 5599
6.3%
5 5156
 
5.8%
Lowercase Letter
ValueCountFrequency (%)
x 177
32.5%
f 177
32.5%
a 177
32.5%
n 7
 
1.3%
o 7
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
F 50
27.2%
A 50
27.2%
X 50
27.2%
N 17
 
9.2%
O 17
 
9.2%
Other Punctuation
ValueCountFrequency (%)
/ 206
75.2%
, 43
 
15.7%
. 24
 
8.8%
: 1
 
0.4%
Space Separator
ValueCountFrequency (%)
161419
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252434
99.7%
Latin 729
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
161419
63.9%
3 13772
 
5.5%
9 12293
 
4.9%
2 11162
 
4.4%
8 10525
 
4.2%
1 9108
 
3.6%
6 8378
 
3.3%
0 7266
 
2.9%
4 6114
 
2.4%
7 5599
 
2.2%
Other values (6) 6798
 
2.7%
Latin
ValueCountFrequency (%)
x 177
24.3%
f 177
24.3%
a 177
24.3%
F 50
 
6.9%
A 50
 
6.9%
X 50
 
6.9%
N 17
 
2.3%
O 17
 
2.3%
n 7
 
1.0%
o 7
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 253163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
161419
63.8%
3 13772
 
5.4%
9 12293
 
4.9%
2 11162
 
4.4%
8 10525
 
4.2%
1 9108
 
3.6%
6 8378
 
3.3%
0 7266
 
2.9%
4 6114
 
2.4%
7 5599
 
2.2%
Other values (16) 7527
 
3.0%
Distinct149
Distinct (%)2.1%
Missing1333
Missing (%)16.0%
Memory size853.9 KiB
2024-10-13T19:26:45.919899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length55
Median length35
Mean length22.368871
Min length12

Characters and Unicode

Total characters156940
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.5%

Sample

1st rowdevdas_chandran@spl.co.in
2nd rowmanish.rathod@jesons.net
3rd rowsunilrao@supertechimpex.com
4th rowmanish.rathod@jesons.net
5th rowsunilrao@supertechimpex.com
ValueCountFrequency (%)
info@visen.net 936
 
13.2%
vijay.chalke@pidilite.co.in 510
 
7.2%
nirmal@nikhiladhesives.com 497
 
7.0%
devdas_chandran@spl.co.in 387
 
5.4%
kiran@cjshahgroup.com 364
 
5.1%
shnair@bhansaliabs.com 351
 
4.9%
asha@chokhanigroup.com 315
 
4.4%
sasee@sanjaychemindia.com 287
 
4.0%
santosh@kljindia.com 282
 
4.0%
sunilrao@supertechimpex.com 240
 
3.4%
Other values (142) 2938
41.3%
2024-10-13T19:26:46.214590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14644
 
9.3%
i 13845
 
8.8%
n 11607
 
7.4%
s 10488
 
6.7%
c 10178
 
6.5%
o 9946
 
6.3%
. 9628
 
6.1%
e 9420
 
6.0%
h 8557
 
5.5%
m 8271
 
5.3%
Other values (50) 50356
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 138721
88.4%
Other Punctuation 16768
 
10.7%
Decimal Number 633
 
0.4%
Connector Punctuation 430
 
0.3%
Uppercase Letter 153
 
0.1%
Space Separator 139
 
0.1%
Dash Punctuation 96
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14644
10.6%
i 13845
 
10.0%
n 11607
 
8.4%
s 10488
 
7.6%
c 10178
 
7.3%
o 9946
 
7.2%
e 9420
 
6.8%
h 8557
 
6.2%
m 8271
 
6.0%
r 6049
 
4.4%
Other values (15) 35716
25.7%
Uppercase Letter
ValueCountFrequency (%)
N 29
19.0%
E 21
13.7%
O 18
11.8%
I 16
10.5%
T 9
 
5.9%
S 8
 
5.2%
L 8
 
5.2%
R 8
 
5.2%
M 8
 
5.2%
P 8
 
5.2%
Other values (8) 20
13.1%
Decimal Number
ValueCountFrequency (%)
2 190
30.0%
1 139
22.0%
5 130
20.5%
0 83
13.1%
9 31
 
4.9%
7 31
 
4.9%
4 12
 
1.9%
6 9
 
1.4%
3 8
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 9628
57.4%
@ 7100
42.3%
/ 29
 
0.2%
: 9
 
0.1%
, 2
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 430
100.0%
Space Separator
ValueCountFrequency (%)
139
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 138874
88.5%
Common 18066
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14644
10.5%
i 13845
 
10.0%
n 11607
 
8.4%
s 10488
 
7.6%
c 10178
 
7.3%
o 9946
 
7.2%
e 9420
 
6.8%
h 8557
 
6.2%
m 8271
 
6.0%
r 6049
 
4.4%
Other values (33) 35869
25.8%
Common
ValueCountFrequency (%)
. 9628
53.3%
@ 7100
39.3%
_ 430
 
2.4%
2 190
 
1.1%
139
 
0.8%
1 139
 
0.8%
5 130
 
0.7%
- 96
 
0.5%
0 83
 
0.5%
9 31
 
0.2%
Other values (7) 100
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14644
 
9.3%
i 13845
 
8.8%
n 11607
 
7.4%
s 10488
 
6.7%
c 10178
 
6.5%
o 9946
 
6.3%
. 9628
 
6.1%
e 9420
 
6.0%
h 8557
 
5.5%
m 8271
 
5.3%
Other values (50) 50356
32.1%
Distinct157
Distinct (%)2.1%
Missing692
Missing (%)8.3%
Memory size910.4 KiB
2024-10-13T19:26:46.467592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length35
Mean length26.621915
Min length6

Characters and Unicode

Total characters203844
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.5%

Sample

1st rowM S RAMACHANDRAN
2nd rowMR DHIRESH GOSALIA
3rd rowSANJIV VASUDEVA
4th rowMR DHIRESH GOSALIA
5th rowSANJIV VASUDEVA
ValueCountFrequency (%)
t.p.ramachandran 936
 
5.6%
m 738
 
4.4%
gupta 680
 
4.0%
sanghavi 547
 
3.2%
tandon 510
 
3.0%
raman 510
 
3.0%
umesh 497
 
2.9%
jayantilal 497
 
2.9%
jinesh 475
 
2.8%
m.shah 475
 
2.8%
Other values (306) 10995
65.2%
2024-10-13T19:26:46.846097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93238
45.7%
A 24137
 
11.8%
N 10332
 
5.1%
R 8113
 
4.0%
H 7655
 
3.8%
M 6956
 
3.4%
S 6480
 
3.2%
I 6152
 
3.0%
T 4856
 
2.4%
L 4389
 
2.2%
Other values (17) 31536
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 107199
52.6%
Space Separator 93238
45.7%
Other Punctuation 3405
 
1.7%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 24137
22.5%
N 10332
9.6%
R 8113
 
7.6%
H 7655
 
7.1%
M 6956
 
6.5%
S 6480
 
6.0%
I 6152
 
5.7%
T 4856
 
4.5%
L 4389
 
4.1%
D 3853
 
3.6%
Other values (13) 24276
22.6%
Other Punctuation
ValueCountFrequency (%)
. 3403
99.9%
, 2
 
0.1%
Space Separator
ValueCountFrequency (%)
93238
100.0%
Decimal Number
ValueCountFrequency (%)
0 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107199
52.6%
Common 96645
47.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 24137
22.5%
N 10332
9.6%
R 8113
 
7.6%
H 7655
 
7.1%
M 6956
 
6.5%
S 6480
 
6.0%
I 6152
 
5.7%
T 4856
 
4.5%
L 4389
 
4.1%
D 3853
 
3.6%
Other values (13) 24276
22.6%
Common
ValueCountFrequency (%)
93238
96.5%
. 3403
 
3.5%
0 2
 
< 0.1%
, 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
93238
45.7%
A 24137
 
11.8%
N 10332
 
5.1%
R 8113
 
4.0%
H 7655
 
3.8%
M 6956
 
3.4%
S 6480
 
3.2%
I 6152
 
3.0%
T 4856
 
2.4%
L 4389
 
2.2%
Other values (17) 31536
 
15.5%
Distinct5808
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Memory size805.7 KiB
2024-10-13T19:26:47.103204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length11
Mean length10.165409
Min length4

Characters and Unicode

Total characters84871
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4646 ?
Unique (%)55.6%

Sample

1st row53,76,423.31
2nd row1,36,563.18
3rd row1,75,043.56
4th row33,53,760.64
5th row6,04,233.66
ValueCountFrequency (%)
62,143.76 25
 
0.3%
2,23,717.54 23
 
0.3%
2,33,750.00 22
 
0.3%
2,24,018.69 19
 
0.2%
2,17,185.37 18
 
0.2%
3,48,661.05 18
 
0.2%
2,21,057.57 17
 
0.2%
2,25,751.59 16
 
0.2%
62,717.29 15
 
0.2%
3,45,838.55 14
 
0.2%
Other values (5798) 8162
97.8%
2024-10-13T19:26:47.472878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 13133
15.5%
. 8349
9.8%
0 8104
9.5%
1 7940
9.4%
2 7226
8.5%
5 6496
7.7%
3 6127
7.2%
4 5809
6.8%
6 5683
6.7%
7 5650
6.7%
Other values (2) 10354
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63389
74.7%
Other Punctuation 21482
 
25.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8104
12.8%
1 7940
12.5%
2 7226
11.4%
5 6496
10.2%
3 6127
9.7%
4 5809
9.2%
6 5683
9.0%
7 5650
8.9%
9 5367
8.5%
8 4987
7.9%
Other Punctuation
ValueCountFrequency (%)
, 13133
61.1%
. 8349
38.9%

Most occurring scripts

ValueCountFrequency (%)
Common 84871
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 13133
15.5%
. 8349
9.8%
0 8104
9.5%
1 7940
9.4%
2 7226
8.5%
5 6496
7.7%
3 6127
7.2%
4 5809
6.8%
6 5683
6.7%
7 5650
6.7%
Other values (2) 10354
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84871
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 13133
15.5%
. 8349
9.8%
0 8104
9.5%
1 7940
9.4%
2 7226
8.5%
5 6496
7.7%
3 6127
7.2%
4 5809
6.8%
6 5683
6.7%
7 5650
6.7%
Other values (2) 10354
12.2%

Tax $
Text

Distinct5163
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Memory size792.1 KiB
2024-10-13T19:26:47.712878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length12
Median length9
Mean length8.5002994
Min length4

Characters and Unicode

Total characters70969
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4126 ?
Unique (%)49.4%

Sample

1st row11,07,328.15
2nd row0.00
3rd row34,107.24
4th row6,90,740.54
5th row1,12,363.29
ValueCountFrequency (%)
0.00 933
 
11.2%
11,185.88 25
 
0.3%
62,048.06 23
 
0.3%
64,830.56 22
 
0.3%
62,131.59 19
 
0.2%
60,236.36 18
 
0.2%
61,310.32 17
 
0.2%
62,758.99 17
 
0.2%
62,612.21 16
 
0.2%
11,289.11 15
 
0.2%
Other values (5153) 7244
86.8%
2024-10-13T19:26:48.063024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 8456
11.9%
. 8349
11.8%
0 7814
11.0%
1 7227
10.2%
2 5973
8.4%
3 5089
7.2%
5 5004
7.1%
4 4771
6.7%
8 4706
6.6%
9 4559
6.4%
Other values (2) 9021
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54164
76.3%
Other Punctuation 16805
 
23.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7814
14.4%
1 7227
13.3%
2 5973
11.0%
3 5089
9.4%
5 5004
9.2%
4 4771
8.8%
8 4706
8.7%
9 4559
8.4%
6 4555
8.4%
7 4466
8.2%
Other Punctuation
ValueCountFrequency (%)
, 8456
50.3%
. 8349
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common 70969
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 8456
11.9%
. 8349
11.8%
0 7814
11.0%
1 7227
10.2%
2 5973
8.4%
3 5089
7.2%
5 5004
7.1%
4 4771
6.7%
8 4706
6.6%
9 4559
6.4%
Other values (2) 9021
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 8456
11.9%
. 8349
11.8%
0 7814
11.0%
1 7227
10.2%
2 5973
8.4%
3 5089
7.2%
5 5004
7.1%
4 4771
6.7%
8 4706
6.6%
9 4559
6.4%
Other values (2) 9021
12.7%
Distinct5906
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Memory size806.4 KiB
2024-10-13T19:26:48.333151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length11
Mean length10.254402
Min length4

Characters and Unicode

Total characters85614
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4778 ?
Unique (%)57.2%

Sample

1st row64,83,751.46
2nd row1,36,563.18
3rd row2,09,150.80
4th row40,44,501.18
5th row7,16,596.95
ValueCountFrequency (%)
73,329.64 25
 
0.3%
2,85,765.60 23
 
0.3%
2,98,580.56 22
 
0.3%
2,86,150.28 19
 
0.2%
2,77,421.73 18
 
0.2%
4,11,420.04 18
 
0.2%
2,82,367.89 17
 
0.2%
2,88,363.80 16
 
0.2%
74,006.40 15
 
0.2%
4,08,089.49 14
 
0.2%
Other values (5896) 8162
97.8%
2024-10-13T19:26:48.738411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 13444
15.7%
. 8349
9.8%
1 7971
9.3%
2 7227
8.4%
3 6691
7.8%
6 6298
7.4%
4 6239
7.3%
0 6071
7.1%
8 6017
7.0%
5 5973
7.0%
Other values (2) 11334
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63821
74.5%
Other Punctuation 21793
 
25.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7971
12.5%
2 7227
11.3%
3 6691
10.5%
6 6298
9.9%
4 6239
9.8%
0 6071
9.5%
8 6017
9.4%
5 5973
9.4%
7 5919
9.3%
9 5415
8.5%
Other Punctuation
ValueCountFrequency (%)
, 13444
61.7%
. 8349
38.3%

Most occurring scripts

ValueCountFrequency (%)
Common 85614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 13444
15.7%
. 8349
9.8%
1 7971
9.3%
2 7227
8.4%
3 6691
7.8%
6 6298
7.4%
4 6239
7.3%
0 6071
7.1%
8 6017
7.0%
5 5973
7.0%
Other values (2) 11334
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 13444
15.7%
. 8349
9.8%
1 7971
9.3%
2 7227
8.4%
3 6691
7.8%
6 6298
7.4%
4 6239
7.3%
0 6071
7.1%
8 6017
7.0%
5 5973
7.0%
Other values (2) 11334
13.2%

Interactions

2024-10-13T19:26:30.555480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:29.279220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:29.710805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:30.142233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:30.663630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:29.390292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:29.821645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:30.249037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:30.767896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:29.499838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:29.928073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:30.355099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:30.868525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:29.603678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:30.034579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T19:26:30.450130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-13T19:26:48.872997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Country of OriginHS CodeImporter CityImporter City- UnifiedImporter PincodeImporter Pincode- UnifiedImporter StateImporter State- UnifiedMonthPort of DestinationRecord IdShipment ModeStandard UnitUnitUnit Rate Currency
Country of Origin1.0000.3840.2400.2400.2910.2920.2840.2830.1470.4240.1630.4880.6730.5310.551
HS Code0.3841.0000.5460.5460.1190.1230.3400.3400.0520.4070.0580.1130.0640.2130.191
Importer City0.2400.5461.0001.0000.9980.9980.9070.9070.0660.4900.0690.4100.5730.4180.386
Importer City- Unified0.2400.5461.0001.0000.9980.9980.9070.9070.0720.4910.0790.4120.5710.4170.387
Importer Pincode0.2910.1190.9980.9981.0001.0000.8720.8720.0520.4400.0150.3350.4210.3090.162
Importer Pincode- Unified0.2920.1230.9980.9981.0001.0000.8720.8720.0530.4400.0070.3370.4200.3090.163
Importer State0.2840.3400.9070.9070.8720.8721.0001.0000.0580.4040.0590.3950.5430.3870.377
Importer State- Unified0.2830.3400.9070.9070.8720.8721.0001.0000.0590.4040.0600.3980.5400.3860.378
Month0.1470.0520.0660.0720.0520.0530.0580.0591.0000.1370.8900.0670.0580.0360.147
Port of Destination0.4240.4070.4900.4910.4400.4400.4040.4040.1371.0000.1870.8700.8150.7200.489
Record Id0.1630.0580.0690.0790.0150.0070.0590.0600.8900.1871.0000.0680.0560.0460.146
Shipment Mode0.4880.1130.4100.4120.3350.3370.3950.3980.0670.8700.0681.0000.4040.4480.484
Standard Unit0.6730.0640.5730.5710.4210.4200.5430.5400.0580.8150.0560.4041.0001.0000.751
Unit0.5310.2130.4180.4170.3090.3090.3870.3860.0360.7200.0460.4481.0001.0000.534
Unit Rate Currency0.5510.1910.3860.3870.1620.1630.3770.3780.1470.4890.1460.4840.7510.5341.000

Missing values

2024-10-13T19:26:31.070322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-13T19:26:31.586323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-13T19:26:32.146831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BE DateHS CodeProduct DescriptionImporter NameImporter Add1Importer CityImporter PincodeImporter StateImporter PhoneImporter E-mailContact PersonExporter NameQTYUnitStandard QtyStandard UnitUnit Rate $Unit Rate In FCUnit Rate CurrencyPort of DestinationShipment ModeCountry of OriginPort Of OriginStandard Unit Rate $MonthRecord IdImporter Name- UnifiedExporter Name- UnifiedImporter Add1- UnifiedImporter City- UnifiedImporter Pincode- UnifiedImporter State- UnifiedImporter Phone- UnifiedImporter E-Mail- UnifiedContact Person-UnifiedEstimated CIF Value $Tax $Landed Value $
34202-04-202229025000STYRENE MONOMER IN BULKSUPREME PETROCHEM LTDSOLITAIRE CORPORATE PARK,BLDG 11 5 TH FLR GURU HARGOVINJI MARG CHAKALAANDHERI WESTMUMBAI400093.0MAHARASHTRA919820037286devdas_chandran@spl.co.inM S RAMACHANDRANSABIC ASIA PACIFIC PTE LTD4,000.40MTS40,00,400.00KGS1.341,343.38USDBombay SeaSeaSaudi ArabiaNa1.34Apr-202229611604SUPREME PETROCHEM LTDSABIC ASIA PACIFIC PTE LTDSOLITAIRE CORPORATE PARK,BLDG 11 5 TH FLR GURU HARGOVINJI MARG CHAKALAANDHERI WESTMUMBAI400093.0MAHARASHTRA919820037286devdas_chandran@spl.co.inM S RAMACHANDRAN53,76,423.3111,07,328.1564,83,751.46
42802-04-202229025000STYRENE MONOMER SMSHIVA PERFORMANCE MATERIALS PRIVATE LIMITEDNaNNaN0.0NaNNaNNaNNaNM S SHELL INTERNATIONAL EASTERN TRA100.00MTS1,00,000.00KGS1.371,365.00USDHaziraSeaSingaporeNa1.37Apr-202232460804SHIVA PERFORMANCE MATERIALS PRIVATE LIMITEDM S SHELL INTERNATIONAL EASTERN TRANaNNaN0.0NaNNaNNaNNaN1,36,563.180.001,36,563.18
44202-04-202229153200VINYL ACETATE MONOMERJESONS INDUSTRIES LIMITED904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WESTMUMBAI400013.0MAHARASHTRA919819883380manish.rathod@jesons.netMR DHIRESH GOSALIASIPCHEM MARKETING COMPANY100.00MTS1,00,000.00KGS1.751,770.00USDHaziraSeaSaudi ArabiaNa1.75Apr-202232459127JESONS INDUSTRIES LIMITEDSIPCHEM MARKETING COMPANY904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WESTMUMBAI400013.0MAHARASHTRA919819883380manish.rathod@jesons.netMR DHIRESH GOSALIA1,75,043.5634,107.242,09,150.80
44302-04-202229025000STYRENE MONOMERSMINEOS STYROLUTION INDIA LIMITED5TH FLOOR OHM HOUSE-II,,OHM BUSINE SS PARK, SUBHANPURA,VADODARA390023.0GUJARAT919076307694sunilrao@supertechimpex.comSANJIV VASUDEVAM S SABIC ASIA PACIFIC PTE LTD2,500.25MTS25,00,250.00KGS1.341,341.25USDHaziraSeaSaudi ArabiaNa1.34Apr-202232460784INEOS STYROLUTION INDIA LIMITEDM S SABIC ASIA PACIFIC PTE LTD5TH FLOOR OHM HOUSE-II,,OHM BUSINE SS PARK, SUBHANPURA,VADODARA390023.0GUJARAT919076307694sunilrao@supertechimpex.comSANJIV VASUDEVA33,53,760.646,90,740.5440,44,501.18
49302-04-202229025000STYRENE MONOMERJESONS INDUSTRIES LIMITED904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WESTMUMBAI400013.0MAHARASHTRA919819883380manish.rathod@jesons.netMR DHIRESH GOSALIASHELL INTERNATIONAL EASTERN TRADING485.14MTS4,85,140.00KGS1.251,245.17USDHaziraSeaSingaporeNa1.25Apr-202232459128JESONS INDUSTRIES LIMITEDSHELL INTERNATIONAL EASTERN TRADING904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WESTMUMBAI400013.0MAHARASHTRA919819883380manish.rathod@jesons.netMR DHIRESH GOSALIA6,04,233.661,12,363.297,16,596.95
55202-04-202229025000STYRENE MONOMERSMINEOS STYROLUTION INDIA LIMITED5TH FLOOR OHM HOUSE-II,,OHM BUSINE SS PARK, SUBHANPURA,VADODARA390023.0GUJARAT919076307694sunilrao@supertechimpex.comSANJIV VASUDEVAM S SABIC ASIA PACIFIC PTE LTD500.05MTS5,00,050.00KGS1.341,341.25USDHaziraSeaSaudi ArabiaNa1.34Apr-202232460774INEOS STYROLUTION INDIA LIMITEDM S SABIC ASIA PACIFIC PTE LTD5TH FLOOR OHM HOUSE-II,,OHM BUSINE SS PARK, SUBHANPURA,VADODARA390023.0GUJARAT919076307694sunilrao@supertechimpex.comSANJIV VASUDEVA6,70,752.111,38,148.118,08,900.22
99202-04-202229024100ORTHOXYLENEJUPITER DYE CHEM PVT LTDMITTAL COURT92/93,A WING 9TH FLR,NARIMAN POINT,MUMBAI400021.0MAHARASHTRA919322884854asha@chokhanigroup.comSELLAPPAN NAGALINGAMTRICON ENERGY LTD100.00MTS1,00,000.00KGS1.001,020.00USDBombay SeaSeaSouth KoreaNa1.00Apr-202229611605JUPITER DYE CHEM PVT LTDTRICON ENERGY LTDMITTAL COURT92/93,A WING 9TH FLR,NARIMAN POINT,MUMBAI400021.0MAHARASHTRA919322884854asha@chokhanigroup.comSELLAPPAN NAGALINGAM99,877.7617,978.001,17,855.76
100102-04-202229025000STYRENE MONOMER IN BULKSUPREME PETROCHEM LTDSOLITAIRE CORPORATE PARK,BLDG 11 5 TH FLR GURU HARGOVINJI MARG CHAKALAANDHERI WESTMUMBAI400093.0MAHARASHTRA919820037286devdas_chandran@spl.co.inM S RAMACHANDRANTOTALENERGIES TRADING ASIA PTE LTD470.72MTS4,70,720.00KGS1.241,244.17USDBombay SeaSeaChinaNa1.24Apr-202229611760SUPREME PETROCHEM LTDTOTALENERGIES TRADING ASIA PTE LTDSOLITAIRE CORPORATE PARK,BLDG 11 5 TH FLR GURU HARGOVINJI MARG CHAKALAANDHERI WESTMUMBAI400093.0MAHARASHTRA919820037286devdas_chandran@spl.co.inM S RAMACHANDRAN5,84,009.841,20,282.667,04,292.50
112102-04-202229153200VINYL ACETATE MONOMERNIKHIL ADHESIVES LTD315, THE SUMMIT BUSINESS BAY,,BH. GURUNANAK PETROL PUMP ANDHERI EMUMBAI400093.0MAHARASHTRA917666986831nirmal@nikhiladhesives.comUMESH JAYANTILAL SANGHAVISIPCHEM MARKETING COMPANY7.22MTS7,220.00KGS2.202,200.00USDHaziraSeaSaudi ArabiaNa2.20Apr-202232459129NIKHIL ADHESIVES LTDSIPCHEM MARKETING COMPANY315, THE SUMMIT BUSINESS BAY,,BH. GURUNANAK PETROL PUMP ANDHERI EMUMBAI400093.0MAHARASHTRA917666986831nirmal@nikhiladhesives.comUMESH JAYANTILAL SANGHAVI15,895.003,216.3519,111.35
116002-04-202229025000STYRENE MONOMERINDISOL MARKETING PVT LTD407 MEADOWS SAHAR PLAZA COMPLEX,AN DHERI KURLA RD J B NGR ANDHERI E ,Mumbai400059.0Maharashtra919664456838niti@indisolglobal.comNITI BHATTKOLMAR GROUP AG0.58MTS580.00KGS1.171,207.50USDHaziraSeaKuwaitNa1.17Apr-202232459237INDISOL MARKETING PVT LTDKOLMAR GROUP AG407 MEADOWS SAHAR PLAZA COMPLEX,AN DHERI KURLA RD J B NGR ANDHERI E ,Mumbai400059.0Maharashtra919664456838niti@indisolglobal.comNITI BHATT679.24140.33819.57
BE DateHS CodeProduct DescriptionImporter NameImporter Add1Importer CityImporter PincodeImporter StateImporter PhoneImporter E-mailContact PersonExporter NameQTYUnitStandard QtyStandard UnitUnit Rate $Unit Rate In FCUnit Rate CurrencyPort of DestinationShipment ModeCountry of OriginPort Of OriginStandard Unit Rate $MonthRecord IdImporter Name- UnifiedExporter Name- UnifiedImporter Add1- UnifiedImporter City- UnifiedImporter Pincode- UnifiedImporter State- UnifiedImporter Phone- UnifiedImporter E-Mail- UnifiedContact Person-UnifiedEstimated CIF Value $Tax $Landed Value $
48878831-03-202329025000STYRENE MONOMERJESONS INDUSTRIES LIMITED904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WESTMUMBAI400013.0MAHARASHTRA919819883380manish.rathod@jesons.netMR DHIRESH GOSALIAKOLMAR GROUP AG525.00MTS5,25,000.00KGS1.151,153.13USDHaziraSeaKuwaitShuaiba1.15Mar-202323841757JESONS INDUSTRIES LIMITEDKOLMAR GROUP AG904 PENINSULA TOWER 1 GANPAT RAO,K ADAM MARG LOWER PAREL WESTMUMBAI400013.0MAHARASHTRA919819883380manish.rathod@jesons.netMR DHIRESH GOSALIA6,05,272.171,28,590.087,33,862.25
48881931-03-202329153200VINYL ACETATE MONOMER 14-17PPM IN BULKVISEN INDUSTRIES LIMITED501, STANFORD, PLOT NO. 554 JN OF S.V.ROAD & JUHU LANE, ANDHERI (W),MUMBAI400058.0MAHARASHTRA022-66443333info@visen.netT.P.RAMACHANDRANCELANESE PTE LTD42.00MTS42,000.00KGS1.081,080.00USDJnptSeaSingaporeOP Singapore1.08Mar-202319381496VISEN INDUSTRIES LIMITEDCELANESE PTE LTD501, STANFORD, PLOT NO. 554 JN OF S.V.ROAD & JUHU LANE, ANDHERI (W),MUMBAI400058.0MAHARASHTRA022-66443333info@visen.netT.P.RAMACHANDRAN45,242.238,143.6153,385.84
48889831-03-202329153200VINYL ACETATE MONOMERIN BULKHARESH PETROCHEM PVT LTD510, ACME PLAZA, ANDHERI KURLA ,ROAD, ANDHERI-(EAST), ,, .Mumbai400059.0MAHARASHTRA28375382/83fax28254417hppl@hareshgroup.comBALASUBRAMANIANICC CHEMICAL CORPORATION50.00MTS50,000.00KGS1.071,070.00USDKandlaSeaTaiwanTaichung1.07Mar-202323926488HARESH PETROCHEM PVT LTDICC CHEMICAL CORPORATION510, ACME PLAZA, ANDHERI KURLA ,ROAD, ANDHERI-(EAST), ,, .Mumbai400059.0MAHARASHTRA28375382/83fax28254417hppl@hareshgroup.comBALASUBRAMANIAN53,393.0014,808.5568,201.55
48905631-03-202329025000STYRENE MONOMER IN BULKINDIAN SYNTHETIC RUBBER PRIVATE LIMITED10TH FLOOR CORE-2,NORTH TOWER,,SCO PE MINAR DISTT CENTRE,` LAXMI NAGAR DELHI,Delhi110092.0DELHI919971008752sachin.sharma@isrpl.co.inTSU TI LIUSABIC ASIA PACIFIC PTE LTD1,571.93MTS15,71,930.00KGS1.091,088.38USDKandlaSeaSaudi ArabiaJubail1.09Mar-202323639094INDIAN SYNTHETIC RUBBER PRIVATE LIMITEDSABIC ASIA PACIFIC PTE LTD10TH FLOOR CORE-2,NORTH TOWER,,SCO PE MINAR DISTT CENTRE,` LAXMI NAGAR DELHI,Delhi110092.0DELHI919971008752sachin.sharma@isrpl.co.inTSU TI LIU17,10,748.993,63,448.6320,74,197.62
48909231-03-202329153200VINYL ACETATE MONOMERMLJP CHEMICALS LIMITED609,NEW DELHI HOUSE 27 BARAKHAMBA ROAD DELHI,DELHI110001.0DELHI919811582387mljpchemicals@mljp.inRUCHI GUPTABRIGHT COAST DEVELOPMENT LIMITED80.00MTS80,000.00KGS1.051,050.00USDKandlaSeaChinaJingjiang1.05Mar-202323387938MLJP CHEMICALS LIMITEDBRIGHT COAST DEVELOPMENT LIMITED609,NEW DELHI HOUSE 27 BARAKHAMBA ROAD DELHI,DELHI110001.0DELHI919811582387mljpchemicals@mljp.inRUCHI GUPTA83,631.6816,922.881,00,554.56
48913331-03-202329025000STYRENE MONOMER IN BULKARYANN CHEMICAL TRADING PRIVATE LIMITEDNaNNaN0.0NaN0NaNNaNSABIC ASIA PACIFIC PTE LTD0.28MTS280.00KGS1.121,156.13USDKandlaSeaSaudi ArabiaJubail1.12Mar-202323824555ARYANN CHEMICAL TRADING PRIVATE LIMITEDSABIC ASIA PACIFIC PTE LTDNaNNaN0.0NaN0NaNNaN313.7768.21381.98
48914231-03-202329025000STYRENE MONOMER GO ARTICLE / ODS / PRODUCT / ITEM IMPORTED VIDE W/H BOE NO.5082369 DT.16.03.2023 BY JESONS TECHNO POLYMERS LLPJESONS TECHNO POLYMERS LLPNaNNaN0.0NaNNaNNaNNaNSHELL INTERNATIONAL EASTERN TRADING184.00MTS1,84,000.00KGS1.151,148.96USDMundrasezSezSingaporeOP Singapore1.15Mar-202324024918JESONS TECHNO POLYMERS LLPSHELL INTERNATIONAL EASTERN TRADINGNaNNaN0.0NaNNaNNaNNaN2,11,366.660.002,11,366.66
48920031-03-202329025000STYRENE MONOMER IN BULK KICEPA REF NO. C010-23-0006307 DT.20.02.2023MODY CHEM401, FAIZ-A-QUTBI, 375, ,NARSINATHA STREET, MASJID (W), ,,Mumbai400009.0MAHARASHTRA66312896modyorganic@gmail.comMR.B.G.GUPTAVINMAR INTERNATIONAL LLC37.00MTS37,000.00KGS1.221,222.50USDKandlaSeaSouth KoreaDaesanseosan1.22Mar-202323479041MODY CHEMVINMAR INTERNATIONAL LLC401, FAIZ-A-QUTBI, 375, ,NARSINATHA STREET, MASJID (W), ,,Mumbai400009.0MAHARASHTRA66312896modyorganic@gmail.comMR.B.G.GUPTA45,115.078,120.7253,235.79
48921631-03-202329153200VINYL ACETATE MONOMERMLJP CHEMICALS LIMITED609,NEW DELHI HOUSE 27 BARAKHAMBA ROAD DELHI,DELHI110001.0DELHI919811582387mljpchemicals@mljp.inRUCHI GUPTABRIGHT COAST DEVELOPMENT LIMITED20.00MTS20,000.00KGS1.051,050.00USDKandlaSeaChinaJingjiang1.05Mar-202323322822MLJP CHEMICALS LIMITEDBRIGHT COAST DEVELOPMENT LIMITED609,NEW DELHI HOUSE 27 BARAKHAMBA ROAD DELHI,DELHI110001.0DELHI919811582387mljpchemicals@mljp.inRUCHI GUPTA20,907.924,230.7225,138.64
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